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Examining potential triggers of algal blooms and harmful algae in the Southern California bight region

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Examining potential triggers of algal blooms and harmful algae in the Southern California bight region

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EXAMINING POTENTIAL TRIGGERS OF
ALGAL BLOOMS AND HARMFUL ALGAE IN
THE SOUTHERN CALIFORNIA BIGHT REGION

Jayme Smith
A Dissertation Presented to the Faculty of the
USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirement for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGICAL SCIENCES)
August 2018

Copyright August 2018 Jayme Smith
2

Approved by Advisory Committee:
David A. Caron (Chair)
David Hutchins
Douglas Capone
Gaurav Sukhatme
3
Dedication
To my grandparents: Mary and Sydney Cross, whom I miss every day. My love of the sea
stems from your influence in my life. You taught me the value of hard work and how to be calm
during chaos. I though of each of you often during graduate school and believe you have been
proud of this work.
4
Table of Contents

Dedication ...................................................................................................................................... 3
Table of Contents .......................................................................................................................... 4
Acknowledgments ......................................................................................................................... 6
Introduction ................................................................................................................................... 9
Introduction References ............................................................................................................. 15
Chapter One: A decade and a half of Pseudo-nitzschia spp. and domoic acid along the coast
of southern California................................................................................................................. 20
Chapter One Abstract .......................................................................................................................... 21
1.1 Introduction ..................................................................................................................................... 22
1.2 Pseudo-nitzschia blooms and domoic acid along the west coast of North America .................. 24
1.3 Pseudo-nitzschia blooms and domoic acid in the Southern California Bight ............................ 27
1.4 Other factors affecting blooms in the Southern California Bight .............................................. 37
1.5 Food web consequences of toxic blooms in the Southern California Bight ............................... 53
1.6 Conclusions and future efforts ...................................................................................................... 56
1.7 Chapter One Figures and Tables .................................................................................................. 59
1.8 Chapter One References ................................................................................................................ 72
Chapter Two: Pseudo-nitzschia species composition varies concurrently with domoic acid
concentrations during two different bloom events in the Southern California Bight .......... 86
Chapter Two Abstract .......................................................................................................................... 87
2.1 Introduction ..................................................................................................................................... 88
2.2 Materials and Methods ................................................................................................................... 90
2.3 Results .............................................................................................................................................. 96
2.4 Discussion ...................................................................................................................................... 104
2.5 Conclusions .................................................................................................................................... 110
2.6 Chapter Two Figures and Tables ................................................................................................ 111
2.7 Chapter Two References .............................................................................................................. 121
Chapter Three: Nearshore wastewater effluent discharge generates three distinct
phytoplankton blooms: A ‘natural’ experiment in Santa Monica Bay, California ............ 129
Chapter Three Abstract ..................................................................................................................... 130
5
3.1 Introduction ................................................................................................................................... 131
3.2 Materials and Methods ................................................................................................................. 134
3.3 Results ............................................................................................................................................ 140
3.4 Discussion ...................................................................................................................................... 153
3.5 Chapter Three Figures and Tables ............................................................................................. 163
3.6 Chapter Three References ........................................................................................................... 175
Supplementary Materials ......................................................................................................... 180
Supplementary Material for Chapter One ....................................................................................... 180
Supplementary Material for Chapter Two ...................................................................................... 190
Supplementary Material for Chapter Three .................................................................................... 193

6
Acknowledgments
The completion of my doctoral dissertation it is a reflection of the people that supported
and mentored me throughout my education. I would like to thank my advisor, David Caron. The
completion of my dissertation work and professional development during graduate school are
due to your mentorship, support, and, dare I say 'captain-ship'. You have helped me become a
better writer, a better speaker, a better reader, a better thinker, and a better scientist. Thank you
for investing your time, funding and energy into my development as a scientist and as a person. I
could never fully express the impact you have made on my life.
Professionally, I am grateful for the time and investment of many individuals. Thank you
to my committee members, past and present, for their insight and time: Dave Hutchins, Doug
Capone, Gaurav Sukhatme, Sergio Sanudo-Wilhelmy, and Dale Kiefer. Thank you to Kate
Hubbard, my accidental collaborator, who has been incredibly generous with her time and
provided scientific and professional advice along the way. Thank you to my labmates who have
infused my graduate school experience with their scientific expertise and a little bit of mischief!
I would like to thank my family and friends who have been my support during graduate
school. Jerren Smith, I owe you a tremendous acknowledgment. Thank you for the unwavering
love, patience, and support during graduate school. You have been amazing. Thank you to my
parents (Mary and Tom Badoud) and brother, David Badoud, you have also been incredibly
supportive and caring during this time. Tara Sirvent, you encouraged me to start me my journey
to graduate school when I was a college freshman at Vanguard University and have never
stopped encouraging me. I would also like to thank my support network of friends Amy Farhina,
Lynnea Morm, Lisa Mesrop, Alle Lie, Sarah Hu, Paige Connell, Ella Sieradzki and Erica
Seubert. I also want to thank my little platoon of undergraduate researchers: Alex Yuen, Noelle
Crowley, and Emily Eggleston – it has been a pleasure to work with each of you!
7
Chapter One was completed with help from my co-authors Paige Connell, Richard H.
Evans, Alyssa G. Gellene, Meredith D.A. Howard, Burton H. Jones, Susan Kaveggia, Lauren
Palmer, Astrid Schnetzer, Bridget N. Seegers, Erica L. Seubert, Avery O. Tatters and David A.
Caron. Additional thanks to Clarissa Anderson and Lillian Busse for providing their published
data sets for inclusion in this work. This work was supported with funding from NOAA National
Centers for Coastal Ocean Sciences Ecology and Oceanography of Harmful Algal Blooms
Program (NA11NOS4780052, NA11NOS4780053, NA11NOS4780030), the Monitoring and
Event Response for Harmful Algal Blooms Program (NA05NOS4781228, NA05NOS4781221,
NA05NOS4781227 NA15NOS4780177, NA15NOS4780204) and the HAB Rapid Event
Response Program (Publication number ECO923, MER210, and ER25), The Environmental
Protection Agency [agreement number GAD# R83-1705], and grants and/or material support
from the USC Sea Grant Program, the Wrigley Institute for Environmental Studies (USC), the
Southern California Coastal Water Research Project, Orange County Sanitation District, the
California Department of Public Health, Hyperion Treatment Plant of the City of Los Angeles,
and the Southern California Coastal Ocean Observing System.
Chapter Two was completed with assistance from my co-authors Alyssa G. Gellene,
Katherine A. Hubbard, Holly A. Bowers, Raphael M. Kudela, Kendra Hayashi, David A. Caron.
This work was supported with funding from the National Oceanic and Atmospheric
Administration ECOHAB grant NA11NOS4780052. Paige Connell, Victoria Campbell, Erica
Seubert and the crews of the R/V Yellowfin and R/V Rachel Carson provided additional sample
collection and processing assistance.
Chapter Three was completed with assistance from my co-authors Carter Ohlmann, Mas
Dojiri, Curtis Cash, Melissa Abderrahim and David A. Caron. Additional assistance was
8
provided J. Jotautas Baronas and Doug Hammond, Avery O. Tatters, the City of Los Angeles
Environmental Monitoring Division and the crews of the M/V La Mer and M/V Marine
Surveyor. Funding for this work was provided by City of Los Angeles.
9
Introduction

Microalgae are primary producers that serve a vital ecological role in marine food webs
through the transfer of energy to higher trophic levels. While microalgae generally benefit higher
trophic levels, some blooms of microalgae can disrupt their local ecosystems through the
production of toxins or other negative ecosystem effects; these events are commonly known as
harmful algal blooms (HABs). The central goal of this dissertation was to examine the
conditions, both natural and anthropogenic that underpin the formation of algal blooms in the
eastern boundary current ecosystem of the Southern California Bight (SCB). In particular, this
dissertation seeks to gain a greater understanding of the conditions that give rise to blooms of the
toxigenic Pseudo-nitzschia genus in the region.
The SCB is a region that extends from Point Conception, California, to Cabo Colnett,
Mexico, along the North American West Coast, and large algal blooms have been observed in
the region for over a century (Torrey, 1902). While a variety of HAB-forming microalgal species
have been documented in the SCB (Caron et al., 2010), the most commonly occurring HAB issue
in the region is the near-annual occurrence of toxigenic blooms of Pseudo-nitzschia. There are
more than 30 described Pseudo-nitzschia species (Lelong et al., 2012; Trainer et al., 2012), and
some species within the genus are capable of producing the neurotoxin domoic acid (DA) (Bates
et al., 1989), which can accumulate in marine food webs (Lefebvre et al., 2002) and result in
widespread wildlife and human health impacts. DA intoxication causes the syndrome known as
Amnesic Shellfish Poisoning, which includes symptoms of confusion, amnesia, diarrhea,
gastrointestinal distress, and in extreme instances, death. As a result, toxic blooms of Pseudo-
nitzschia often result in mass strandings and mortalities of marine mammals and birds due to
neurological damage (Fritz et al., 1992; Kvitek et al., 2008; Lefebvre et al., 2002; Scholin et al.,
10
2000). These events can also necessitate fishery closures, such as the closure of the Dungeness
crab fishery in 2015 that resulted in economic losses of approximately 100 million U.S. dollars
(Lowther and Liddel, 2016).
The factor(s) that stimulate cell growth and DA production by Pseudo-nitzschia species
vary between and within species and may be multiple. (Lelong et al., 2012; Thessen et al., 2009;
Trainer et al., 2012). Nutrient availability (e.g. phosphorus and silicate limitation) (Fehling et al.,
2004; Pan et al., 1996a; Pan et al., 1996b) and the form of nitrogen (Cochlan et al., 2008;
Howard et al., 2007; Kudela et al., 2008; Radan and Cochlan, 2018; Thessen et al., 2009) have
yielded a variety of growth and DA responses. Other factors, including temperature (Amato et
al., 2010; Cho et al., 2001; McCabe et al., 2016; Santiago-Morales and García-Mendoza, 2011;
Tatters et al., 2018; Thorel et al., 2014; Zhu et al., 2017), partial CO
2
concentration (Tatters et
al., 2012; Tatters et al., 2018), irradiance (Thorel et al., 2014), and salinity (Thessen et al., 2005)
have also been shown to dramatically alter growth and toxin production.
Blooms of Pseudo-nitzschia have become an annual occurrence along the West Coast of
the United States for over two decades (Lewitus et al., 2012). Initially, such blooms occurred
primarily in the Pacific Northwest and northern to central Californian coastlines, but DA was not
documented in the region until the early 2000’s (Busse et al., 2006; Schnetzer et al., 2007)
despite observations of Pseudo-nitzschia in planktonic communities for decades (Lange et al.,
1994). The factors related to the increased occurrence of toxigenic Pseudo-nitzschia in the SCB
since approximately 2003 are relatively unknown. In Chapter One of this dissertation, I
conducted a synthesis data from coastal monitoring projects and major field campaigns
conducted from 2003 to 2017 that documented Pseudo-nitzschia abundances and domoic acid in
the SCB region in order to better understand the factors that promote the formation of toxigenic
11
Pseudo-nitzschia blooms in the region. Chapter One highlights the prevalence of DA in the SCB,
showing that toxin was measured in the plankton (e.g. particulate DA) and in the shellfish every
year from 2003 to 2017. However, significant year-to-year variability was observed in DA
concentrations in the plankton and in shellfish. Overall, particulate DA concentrations in the
SCB have met or exceeded some of the highest concentrations ever recorded in the literature,
particularly during 2003, 2006, 2007, 2011 and 2017. Similarly, some of the highest
concentrations of DA in shellfish tissue over the last 15 years in the state of California were
measured in the northern regions of the SCB. Additionally, a strong link between measurements
of particulate DA in the water column and mass mortality events of marine mammals and
seabirds was demonstrated in the region.
Toxigenic blooms in the SCB exhibit a strong seasonality, with maximal DA
measurements generally occurring between March and May during the last 15 years. The
seasonality of toxigenic blooms appears to be tied, in part, to the occurrence of seasonal,
springtime upwelling in the Bight. High concentrations of DA and elevated abundances of
Pseudo-nitzschia generally tend to coincide with cooler waters and more saline waters, indicative
of the influence of upwelling. The relationship between the development of toxigenic Pseudo-
nitzschia blooms and upwelling events appear to follow two possible mechanisms. In one
mechanism upwelling provides nutrients to surfaces waters, and following a period of days, a
Pseudo-nitzschia bloom develops. Alternatively, upwelling appears to advect subsurface Pseudo-
nitzschia populations to surface waters, which gives rise to an almost ‘instant’ bloom and toxic
conditions. In addition to upwelling, temperature appears to demonstrate a controlling influence
of on the development and toxicity of Pseudo-nitzschia blooms in the region. Particulate DA and
elevated abundances of Pseudo-nitzschia were generally not detected at temperatures higher than
12
20°C, which suggests that temperatures above that threshold may exhibit a restrictive effect on
the development of blooms in the region.
Chapter Two of this dissertation expands our understanding of the factors that may give
rise to the interannual variability of DA concentrations in the region that was highlighted in
Chapter One. A field campaign was conducted in the spring of 2013 and 2014 with the goal of
observing the initiation of toxigenic Pseudo-nitzschia blooms and the associated environmental
conditions. Biological and chemical samples were collected during weekly surveys across an
onshore to offshore transect in the central Bight. Blooms of similar cell abundances of Pseudo-
nitzschia were observed in 2013 and 2014, but maximal DA concentrations observed in 2013
were almost two orders of magnitude higher than in 2014.
A comparison of environmental factors, including upwelling, was conducted to determine
the factors influencing the difference in maximal DA concentration observed between the two
blooms. Overall, the environmental conditions between years were relatively similar. A select
number of samples were analyzed to examine the species that populated the Pseudo-nitzschia
assemblage. These results suggested that variability in toxin concentrations observed between
2013 and 2014 was most closely related to the species of Pseudo-nitzschia in the assemblage
during each bloom and the inherent differences in toxin production by those species. The
Pseudo-nitzschia assemblage during the 2013 bloom was dominated by P. australis/P. seriata,
while the assemblage in 2014 was a mix of several minimally- and non-toxigenic species.
Overall, the factors that lead to the to the growth of specific Pseudo-nitzschia species within each
natural assemblage were not able to be determined within the context of Chapter Two. Several
studies have begun to describe relationships between Pseudo-nitzschia species dynamics and
physiochemical factors such as temperature, nutrient concentrations, and nutrient ratios (Bates et
13
al., 1998; Fernandes et al., 2014; Guannel et al., 2015; Hubbard et al., 2014; Ruggiero et al.,
2015) but, overall, these relationships are complex, and appear to be somewhat regionally
specific. Further work to understand the factors controlling the Pseudo-nitzschia species
dynamics in the Bight will aid in understanding and predicting the toxigenic events in the future.
On a region-wide scale, the SCB is heavily influenced by natural nitrogen input via
upwelling, but recent work has shown that anthropogenic nitrogen flux almost matches that of
upwelling on a sub-regional scale in the most heavily populated regions of the SCB coast
(Howard et al., 2014). The role of anthropogenically-introduced nitrogen is poorly understood its
effect on nutrient ratios and biological dynamics in the region is not well understood. Given that
anthropogenic nitrogen flux is predominantly in the form of ammonium, whereas upwelling is
nitrate dominated, the role of anthropogenic nitrogen in the initiation of toxigenic Pseudo-
nitzschia blooms in the region is important, but relatively uncharacterized. In Chapter Three of
this dissertation, I attempted to address this question through the study of the microalgal
response to a wastewater diversion conducted by Hyperion Treatment Plant (HTP), which
discharges into Santa Monica Bay.
HTP is one of the largest Publicly Owned Treatment Works (POTWs) on the West Coast,
discharging approximately 8.5 x 10
8
L day
-1
of secondarily treated wastewater (effluent) into
Santa Monica Bay. Ammonium and phosphorus concentrations in the secondarily-treated
effluent are approximately three orders of magnitude higher than the ambient concentrations in
the receiving waters. Under normal operations, HTP discharges effluent from a pipe that is 8.1
km offshore at a depth of 57 m, below the euphotic zone. Infrastructure repairs to HTP’s 8.1 km
outfall in autumn 2015 necessitated the diversion effluent discharge through an auxiliary 1.2 km
14
outfall located at a depth of 18 m. These repairs necessitated in the sustained discharge of
effluent into the nearshore coastal region of Santa Monica Bay for six weeks.
The impact of the nearshore effluent discharge on the microplankton community was
determined from weekly surveys that were conducted prior to, during, and after the diversion.
During the course of the diversion, three large phytoplankton blooms occurred, each dominated
by different taxa, and each occurring in different geographic regions of Santa Monica Bay.
Maximum surface chlorophyll values ranging from 20 to >195 µg L
-1
were observed during
bloom events, which far exceeded regional bloom thresholds (Kim et al., 2009; Seubert et al.,
2013). Bloom events were temporally distinct and were dominated by diatoms, marine
euglenoids, and the potential HAB-forming raphidophytes Chattonella marina, in succession.
Notably, this work is the first reported occurrence of high abundances (>10
4
cells L
-1
) of
Chattonella marina in the region.
While Pseudo-nitzschia spp. were observed throughout the diversion, they occurred at
relatively low abundances compared to other phytoplankton genera. However, elevated
abundances of Pseudo-nitzschia and low concentrations of DA were observed at elevated
abundances (>10
4
cells L
-1
), particularly in the survey conducted three days after the diversion
ended. It is likely that Pseudo-nitzschia spp. did not grow well during the diversion due to
surface water temperatures exceeding 20°C. However, due to seasonal cooling, water
temperatures decreased by several degrees following the end of the diversion, which likely
allowed the growth of Pseudo-nitzschia.

15
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nitzschia species (Bacillariophyceae) Growth and Distribution. J. Phycol. 41(1), 21-29.
Thorel, M., Fauchot, J., Morelle, J., Raimbault, V., Le Roy, B., Miossec, C., Kientz-Bouchart,
V., Claquin, P., 2014. Interactive effects of irradiance and temperature on growth and
domoic acid production of the toxic diatom Pseudo-nitzschia australis
(Bacillariophyceae). Harmful Algae 39, 232-241.
Torrey, H.B., 1902. An unusual occurrence of dinoflagellata on the California coast. The
American Naturalist 36(423), 187-192.
Trainer, V.L., Bates, S.S., Lundholm, N., Thessen, A.E., Cochlan, W.P., Adams, N.G., Trick,
C.G., 2012. Pseudo-nitzschia physiological ecology, phylogeny, toxicity, monitoring and
impacts on ecosystem health. Harmful Algae 14, 271-300.
19
Zhu, Z., Qu, P., Fu, F., Tennenbaum, N., Tatters, A.O., Hutchins, D.A., 2017. Understanding the
blob bloom: Warming increases toxicity and abundance of the harmful bloom diatom
Pseudo-nitzschia in California coastal waters. Harmful Algae 67, 36-43.

20
Chapter One: A decade and a half of Pseudo-nitzschia spp. and domoic acid
along the coast of southern California

Jayme Smith
1
, Paige Connell
1
, Richard H. Evans
2
, Alyssa G. Gellene
1
, Meredith D.A. Howard
3
,
Burton H. Jones
4
, Susan Kaveggia
5
, Lauren Palmer
6
, Astrid Schnetzer
7
, Bridget N. Seegers
8,9
,
Erica L. Seubert
1
, Avery O. Tatters
1
and David A. Caron
1

1
Department of Biological Sciences, 3616 Trousdale Parkway, AHF 301, University of Southern
California, Los Angeles, CA 90089
2
Pacific Marine Mammal Center, 20612 Laguna Canyon Rd., Laguna Beach, CA 92651
3
Southern California Coastal Water Research Project, 3535 Harbor Blvd., Costa Mesa, CA
92626
4
KAUST, Red Sea Research Center, King Abdullah University of Science and Technology, 4700
King Abdullah Boulevard, Thuwal, 23955-6900, Saudi Arabia
5
International Bird Rescue, 3601 S Gaffey St, San Pedro, CA 90731
6
Marine Mammal Care Center, 3601 S. Gaffey St., San Pedro, CA 90731
7
North Carolina State University, 4248 Jordan Hall, 2800 Faucette Drive, Raleigh, NC 276958
8
National Aeronautics and Space Administration, Goddard Space Flight Center, Mail Code
616.2, Greenbelt, MD, 20771.
9
GESTAR/Universities Space Research Association, 7178 Columbia Gateway Drive, Columbia,
MD 21046

This work has been accepted for publication in Harmful Algae.
21
Chapter One Abstract
Blooms of the marine diatom genus Pseudo-nitzschia that produce the neurotoxin domoic
acid have been documented with regularity along the coast of southern California since 2003,
with the occurrence of the toxin in shellfish tissue predating information on domoic acid in the
particulate fraction in this region. Domoic acid concentrations in the phytoplankton inhabiting
waters off southern California during 2003, 2006, 2007, 2011 and 2017 were comparable to
some of the highest values that have been recorded in the literature. Pseudo-nitzschia blooms
have exhibited strong seasonality, with toxin appearing predominantly in the spring. Year-to-
year variability of particulate toxin has been considerable, and observations during 2003, 2006,
2007, 2011 and again in 2017 linked domoic acid in the diets of marine mammals and seabirds to
mass mortality events among these animals. Here we review information collected during the
past 15 years documenting the phenology and magnitude of Pseudo-nitzschia abundances and
domoic acid within the Southern California Bight. The general oceanographic factors leading to
blooms of Pseudo-nitzschia and outbreaks of domoic acid in this region are clear, but subtle
factors controlling spatial and interannual variability in bloom magnitude and toxin production
remain elusive.

22
1.1 Introduction
The Southern California Bight (SCB) is a major portion of the western boundary of North
America and the U.S. west coast. The feature is generally defined as an approximately 700 km
coastline extending from Point Conception, California south to beyond the U.S. border (Figure
1.1)(Hickey, 1992). The physical oceanography of the Bight is complex, and is distinct from the
coastal ocean to the north of Point Conception, and the California Current to the west; yet, this
region shares a degree of continuity with, and influence from these features. The Channel Islands
throughout the Bight act to buffer the southern California coast from much of the direct impact
of the otherwise oceanic California Current, as well as moderate meteorological effects along the
coast. The curved orientation of the SCB, compared to the north-south trending coastline of the
rest of the west coast, also acts to buffer the southern coast of California to prevailing winds.
A Mediterranean-type climate dominates throughout the SCB. Annual average daily highs in
air temperatures in the Los Angeles area are ≈20-26˚C, while annual sea surface temperatures
generally range ≈14-21˚C. Wind events, rainfall and river discharges are highly seasonal. The
majority of the rainfall in the region occurs during winter months, and strong wind and upwelling
events are dominant during winter and spring. Within seasons, these events are episodic and
short-lived, generally lasting a few days. Average annual rainfall in the region is historically low
(< 40 cm) but interannual variability in rainfall can be great. An ‘extreme-to-exceptional’
drought during 2012-2016 was followed by the highest average rainfall in the state in 122 years
during the winter of 2016-2017 (http://www.latimes.com/local/lanow/la-me-g-california-
drought-map-htmlstory.html)(Griffin and Anchukaitis, 2014).
The SCB is a coastal ocean region of extensive economic, environmental and cultural
importance as well as increasing human impact. The population of the Los Angeles-Long Beach
23
area alone was estimated at >18 million people in 2015 (Annual Estimates of the Resident
Population: April 1, 2010 to July 1, 2015; Source: U.S. Census Bureau, Population Division;
Release Date: March 2016). Beach visitations in the SCB a decade ago averaged approximately
129 million per year (Dwight et al., 2007), and have almost certainly increased concurrently with
the population over the last decade. Coastal property development throughout most of the region
is extensive, as are commercial and other activities. The ports of Los Angeles and Long Beach
are the two busiest ports in the U.S. and together handle over 400 billion USD in trade annually
(http://labusinessjournal.com/news/2017/jul/17/june-imports-surge-l-long-beach-ports/).
Additionally, some coastal areas in the SCB have significant agricultural activities.
Nutrient loading within the SCB differs greatly throughout the region and these
differences have potentially important impacts on algal blooms. Land use varies significantly
within the SCB, from largely undeveloped land (e.g. from San Clemente to Oceanside, San
Diego County) to areas draining a mixture of agricultural and urban landscape (e.g. Ventura
County) to highly urbanized regions (Los Angeles and Orange Counties). This mosaic of land
use results in variability in the magnitude and type of nutrient loading to the coastal ocean, but
anthropogenic nutrients appear to constitute significant sources of growth-stimulating nutrients
for coastal phytoplankton in some regions (Howard et al., 2014; Kudela et al., 2008; Reifel et al.,
2013). Recent studies suggest these inputs may have ramifications for the resilience of coastal
ecosystems (Capone and Hutchins, 2013) and have significantly affected near-shore waters in
these urban areas. For example, Howard et al. (2014) reported that wastewater discharge from
Publicly Owned Treatment Works (POTWs) in highly urbanized areas of the SCB contributed
similar amounts of nitrogen to nearshore coastal ecosystems as wind-driven upwelling events,
which was the most significant source of nitrogen. Additionally, nitrification of ammonium from
24
wastewater effluent has been shown to provide a significant source of nitrogen utilized by the
biological community (McLaughlin et al., 2017). Increased anthropogenic input to ocean
ecosystems is not unique to the SCB or the impact of POTWs, but rather appears to be a growing
problem globally (Ren et al., 2017). The full impact of increased anthropogenic input on
phytoplankton communities in the Bight has been difficult to characterize because there are no
phytoplankton biomass data that predate POTWs discharges in the region. However, the findings
of Howard et al. (2014) and McLaughlin et al., (2017) along the southern California coast are
consistent with the observations of Nezlin et al. (2012) that algal bloom ‘hot spots’ along the
coast were co-located with POTW outfalls. Additionally, on small spatial scales, wastewater
effluent and terrestrial runoff have been shown to increase phytoplankton biomass and affect
patterns of phytoplankton productivity and community composition (Corcoran et al., 2010;
Reifel et al., 2013). Additionally, studies in the SCB have concluded that patterns of chlorophyll
variability and productivity in the nearshore coastal waters are not always attributed to classical
coastal upwelling (Corcoran et al., 2010; Kim et al., 2009; Nezlin et al., 2012).
1.2 Pseudo-nitzschia blooms and domoic acid along the west coast of North America
The west coast of North America has been the site of a few well-documented harmful algal
bloom (HAB) issues, as well as some emerging ones (Lewitus et al., 2012). These issues include
a long history of paralytic shellfish poisoning (PSP; caused by saxitoxin contamination) along
the northwestern U.S. coast and Canada dating to the 1700s, while outbreaks of amnesic shellfish
poisoning (ASP; caused by domoic acid) have only been documented more recently along the
west coast. Considerable environmental and seafood monitoring has been, and continues to be
conducted along the western coast of North America due to the threat that these toxins pose to
human and animal health.
25
Extensive field studies to understand the environmental factors leading to Pseudo-nitzschia
blooms and domoic acid events along the northern sector of the west coast have been conducted
off Washington state (Trainer et al., 2007; Trainer et al., 2003), and to a lesser extent off Oregon
(Du et al., 2016; McKibben et al., 2015) during the past few decades. Studies in coastal waters
off Washington have characterized the Juan de Fuca eddy, located offshore from the mouth of
the Juan de Fuca Straight, as a ‘hot spot’ for the development of toxic Pseudo-nitzschia blooms
(Trainer et al., 2002; Trainer et al., 2009). Contamination of beaches and inlets along the
Olympic Peninsula of Washington and Vancouver Island, British Columbia results when toxic
waters from the eddy are transported onshore by prevailing weather and oceanographic
conditions (Trainer et al., 2009).
Mexican coastal waters south of California have been less well-characterized with respect to
Pseudo-nitzschia blooms and toxic events attributable to domoic acid, in contrast to findings
along the U.S. and Canadian coasts. However, multiple Pseudo-nitzschia species, including some
toxic ones, have been reported from the coast of Baja California (García-Mendoza et al., 2009;
Hernández-Becerril, 1998), and at least one published report to date has linked an animal
mortality event in the region to the toxin (Sierra Beltrán et al., 1997).
Phytoplankton blooms along the coast of California have historically included a number of
potentially harmful algae, including toxin-producing diatom species within the genus Pseudo-
nitzschia (Buck et al., 1992; Fryxell et al., 1997; Lange et al., 1994). Other harmful species
known to live along the California coast include numerous dinoflagellates (Lingulodinium
polyedrum (Howard et al., 2008; Torrey, 1902), Akashiwo sanguinea (Jessup et al., 2009),
Prorocentrum micans (Gregorio and Pieper, 2000), Cochlodinium fulvescens (Howard et al.,
2012; Kudela and Gobler, 2012), Alexandrium catenella (Garneau et al., 2011; Jester et al.,
26
2009) and Dinophysis spp.), and the raphidophytes Heterosigma akashiwo, Chattonella marina
and Fibrocapsa japonica (Caron et al., 2010; Gregorio and Connell, 2000; Herndon et al., 2003;
O’Halloran et al., 2006).
Paralytic shellfish poisoning along the California coast has been a long-standing health
concern (Meyer et al., 1928), as it has farther north along the California coastline and in the
Pacific Northwest (PNW); however, awareness of toxic events attributable to domoic acid is a
more recent concern that has been documented along the California coast only within the last
two decades. A seabird mortality event caused by domoic acid poisoning along the California
coast was first linked to a Pseudo-nitzschia bloom in 1991 off central California (Work et al.,
1993), and subsequently to a marine mammal mass mortality event in that region (Scholin et al.,
2000). Circumstantial evidence exists that toxic Pseudo-nitzschia blooms, and mass mortality
events resulting from these blooms, may have occurred for years prior to that date in and around
Monterey Bay (Buck et al., 1992; Fritz et al., 1992; Greig et al., 2005; Walz et al., 1994). Kudela
et al. (2003) identified four major blooms of Pseudo-nitzschia off central California during 1991,
1998, 2000, and 2002. Regardless of its role prior to the 1990s, domoic acid poisoning has been
documented since then as a recurring threat along the entire coastline of California. Data
collected by the California Department of Public Health indicate that domoic acid has been
detected in shellfish tissue in virtually all years from 2003 to 2016, although the magnitude and
geographic extent of the toxin has varied considerably by year and county (Figure 1.2). In
particular, counties in central California (north of the SCB) and the northern counties within the
SCB (Santa Barbara and Ventura counties) have experienced the highest concentrations and most
frequent occurrences of domoic acid contamination of shellfish (Figure 1.2B,C).
27
1.3 Pseudo-nitzschia blooms and domoic acid in the Southern California Bight
Reports implicating toxic Pseudo-nitzschia blooms in large mortality events for seabirds and
marine mammals off central California (Scholin et al., 2000; Work et al., 1993) and seabirds off
Baja California (Sierra Beltrán et al., 1997) during the 1990s were followed by an unusual
marine mammal mortality event in the SCB during 2002 that was eventually attributed to domoic
acid poisoning (Torres de la Riva et al., 2009). These reports linked domoic acid in the
particulate fraction of the plankton to animal mortality events, but they were by no means the
first documentation of Pseudo-nitzschia blooms in the region. Lange et al. (1994) analyzed
historical data of phytoplankton communities collected off Scripps Pier in La Jolla, located in
San Diego County. Lange et al. (1994) reported high abundances of ‘Nitzschia seriata’ (later
recognized as a member of the genus Pseudo-nitzschia) present in the plankton throughout the
1930s, and sporadically through subsequent years, although no toxic episodes attributable to
these diatoms were documented. Species of ‘Nitzschia’ were also a common occurrence in
Monterey Bay decades before toxin events there were attributed to this diatom group (Bolin and
Abbott, 1962). An analysis of Pseudo-nitzschia frustules and domoic acid in a collection of
sediment trap samples from the Santa Barbara Channel dating to 1993 also demonstrated that the
toxin was present in the region prior to the 2002 toxic event (Sekula-Wood et al., 2011). Barron
et al. (2010) reported on analyses of sediment cores that revealed increased abundances of P.
australis in the Santa Barbara Basin beginning around 1985.
1.3.1 Interannual variability in domoic acid in the SCB since 2002
Several studies since the 2002 mortality event have documented the presence and
concentrations of particulate domoic acid in coastal waters of the Southern California Bight. A
summary of that information (≈4,500 measurements) indicates that domoic acid was present in
28
virtually all years, although there was significant year-to-year variability in maximal particulate
concentrations observed (Figure 1.3, Supplemental Figure 1.1), as well as considerable spatial
variability in the distribution of toxin in shellfish samples along the length of the SCB coastline
(Figure 1.2C,D). Domoic acid was documented throughout the Bight in 2003, undoubtedly a
result of the increased awareness and monitoring efforts following the 2002 mortality event.
High concentrations (>10 µg L
-1
) particulate domoic acid were observed during spring 2003 in
the San Pedro Basin around and within the mouth of Los Angeles Harbor (Schnetzer et al.,
2007), with lower concentrations documented to the north in the Santa Barbara Channel
(Anderson et al., 2006) and to the south off San Diego (Busse et al., 2006).
In contrast to 2002 and 2003, relatively low concentrations of particulate domoic acid
(generally ≤2 µg L
-1
) were observed Bight-wide during 2004 for those regions that were studied
(Anderson et al., 2009; Busse et al., 2006; Schnetzer et al., 2007). Domoic acid concentrations
were also generally low during 2005 in most of the Southern California Bight. Coincidentally, a
massive summer-fall bloom of the dinoflagellate, Lingulodinium polyedrum occurred along the
entire coastline of the SCB during 2005 (Howard et al., 2008). Only the Santa Barbara Channel
exhibited significant concentrations of particulate domoic acid during that year, with two
exceptionally high values for the SCB, ≈18 and ≈50 µg L
-1
observed (Anderson et al., 2009).
Toxin concentrations in Santa Monica Bay and the San Pedro Basin were generally extremely
low or below the limit of detection (0.01 µg L
-1
or 0.02 µg L
-1
, depending on methodology) for
the methods used at that time (Anderson et al., 2009; Schnetzer et al., 2013; Shipe et al., 2008).
Substantial amounts of particulate toxin reappeared during 2006 and 2007 in both the Santa
Barbara Channel and the San Pedro Basin (Anderson et al., 2009; Schnetzer et al., 2013), and
massive mortality/stranding events involving large numbers of marine mammals and seabirds
29
were associated with the appearance of particulate domoic acid during both years in the SCB
(see Section 5 below). The years between 2008 and 2016 did not experience toxin-related
mortality events of the magnitude observed during 2006 and 2007, but measurable quantities of
domoic acid occurred in all years through 2013 (Figure 1.3), and animal mortalities attributable
to the toxin were recorded.
Concentrations of particulate domoic acid during the following three years (2014-2016)
remained remarkably and consistently low, given the constancy with which the toxin occurred
during the previous decade (Figure 1.3), and given the unprecedented Pseudo-nitzschia bloom
and domoic acid event that occurred during 2015 along the west coast of North America from
just north of the Southern California Bight to Alaska (McCabe et al., 2016; Ryan et al., 2017).
Interestingly, 2014-2016 corresponded to an unprecedented drought across southern California
and much of the southwestern U.S. (Flint et al., 2018). Particulate domoic acid concentrations of
the Southern California Bight returned to substantial levels during spring 2017 (>5 µg L
-1
) within
both southern and northern regions (Figure 1.3). The bloom resulted in significant marine
mammal and seabird mortality events whose impacts are still being investigated.
An ongoing weekly plankton-monitoring program established at several piers within the SCB
in 2008 has provided uninterrupted documentation since that time of Pseudo-nitzschia
abundances and the occurrence of domoic acid (http://www.sccoos.org/data/habs/). This time-
series, and a number of ship-based studies conducted throughout the past decade, have
documented substantial and sporadically exceptional concentrations of particulate domoic acid in
the SCB. The highest values to date of particulate domoic acid in the Bight were reported during
2011 by Stauffer et al. (2012) from a small number of samples collected in the central San Pedro
Channel. A few of the values exceeded 50 µg L
-1
, rivaling some of the highest values of
30
particulate domoic acid ever recorded from natural plankton communities. However, the bloom
was very short-lived and was not implicated in significant numbers of animal mortalities.
Shipboard studies during the past decade have also shed light on the complexity of Pseudo-
nitzschia blooms and domoic acid events in the region. Seegers et al. (2015) documented the
potential importance of subsurface populations of Pseudo-nitzschia during 2010, suggesting that
populations were maintained in the subsurface chlorophyll maxima. The authors reported results
indicating a role for these subsurface populations of Pseudo-nitzschia along the San Pedro Shelf
(Figure 1.1) in ‘seeding’ surface blooms during coastal upwelling events. Advection of
subsurface populations may also contribute to rapid increases in domoic acid concentrations
during these events as toxin-producing cells are upwelled into surface waters (see Section 3.3
below). Most recently, Smith et al. (2018) conducted extensive sampling on and off the San
Pedro Shelf near the city of Newport Beach during 2013 and 2014. Similar bloom abundances of
Pseudo-nitzschia cells were observed in both years during that study, yet particulate domoic acid
concentrations in the two years differed by two orders of magnitude. The authors presented
evidence that differences in species/strain composition among potential bloom-forming Pseudo-
nitzschia species was an important determinant of particulate domoic acid concentrations during
the spring along the San Pedro Shelf.
Analysis of these time-series and shipboard datasets, and others from Santa Monica Bay and
the San Pedro Shelf (Anderson et al., 2011; Anderson et al., 2009; Schnetzer et al., 2013;
Schnetzer et al., 2007; Seubert et al., 2013; Smith et al., 2018) have yielded only weak
correlations between physical-chemical parameters or chlorophyll, and the abundances of
Pseudo-nitzschia and domoic acid concentration (see Section 4 below). Thus, the specific factors
that might preferentially stimulate the growth of toxic Pseudo-nitzschia species that occur in the
31
region remain enigmatic. A variety of species in this genus have been identified (or implicated)
in the appearance of domoic acid in the particulate fraction of the plankton and marine food webs
in the Southern California Bight during the past 15 years. These include P. australis, P. pungens,
P. multiseries and P. pseudodelicatissima (Horner et al., 1997), although P. australis has been
implicated most often in major toxic events. Nevertheless, it appears that toxic events in the
region may be attributable to a number of toxic species rather than a single reoccurring species,
and the specific factors leading to the growth of these different species are poorly understood.
Such mixed assemblages of Pseudo-nitzschia appear to be the rule rather than the exception, and
may explain the significant year-to-year variability in toxin concentrations that have been
observed in multi-year monitoring datasets. Similar findings (multiple Pseudo-nitzschia species
and year-to-year variability in toxin concentrations) have been reported from a decade of
monitoring off New Zealand by Rhodes et al. (2013).
1.3.2 Seasonality of domoic acid in the SCB
A synthesis of monthly averages and maxima for domoic acid concentration in shellfish
tissue along the entire coast of California during the past 15 years (Figure 1.4) indicates that
domoic acid has occurred in all seasons statewide, although highest averages and maximal tissue
concentrations tended to occur during spring and lowest values in winter (Figure 1.4A). The
seasonal pattern for the northern counties within the Southern California Bight (Santa Barbara
and Ventura Counties) exhibited an overall pattern for both monthly averages and maximal
values of domoic acid in shellfish tissue that were similar to the overall seasonal pattern
statewide (Figure 1.4B). However, monthly averages in the northern counties of the SCB were
generally greater than averages determined across the entire state, and the highest monthly
maxima statewide tended to occur within the northern counties of the SCB.
32
The seasonal pattern of domoic acid in shellfish tissue observed in Santa Barbara and
Ventura Counties was reflected in the monthly distribution of maximal particulate domoic acid
concentrations observed during each year since 2003 (colored arrows in Figure 1.4B). Maximal
particulate domoic acid concentrations for most years occurred during April for 7 of 15 annual
maxima (Anderson et al., 2006) but maximal annual particulate domoic acid concentrations also
occurred at least once in all months except February, March, October, and December. Data for
particulate domoic acid concentrations (colored arrows in Figure 1.4) were based on ≈4,500 data
points (the distribution of samples by month is shown in Supplemental Figure 1.2), and maximal
values for each month in the datasets from the northern and southern counties are given in
Supplemental Table 1.1.
In contrast, the seasonal occurrence of domoic acid in the particulate fraction of the plankton
and in shellfish tissue along the coast of the southern counties (Los Angeles, Orange, and San
Diego counties; Figure 1.4C) within the SCB during the past 15 years was strikingly different
than the statewide pattern, or the pattern observed in the northern counties of the SCB. First, the
southern counties generally experienced substantially lower maximal concentrations of domoic
acid in shellfish tissue relative to the rest of the state, particularly compared to values observed in
the northern counties of the SCB (note different ranges on the Y axes in Figures 4A,B versus
4C). Second, substantive values of shellfish contamination were largely associated with spring
months in the southern counties (March-May) indicating much stronger seasonality in the
appearance of the toxin along the coast of those counties.
Strong seasonality in the southern counties of the SCB is substantiated by examination of the
month in each year that experienced the maximal concentration of particulate domoic acid along
the coast of the southern counties (colored arrows in Figure 1.4C). Maximal particulate domoic
33
acid was observed in March, April or May in 13 of 15 years since 2003 in the southern counties
of the SCB. Only two years had maxima that occurred in other months, and those were years that
experienced relatively moderate or very low overall particulate domoic acid concentrations. A
maximal value of ≈2 µg L
-1
was observed in February 2004, and a maximal value of 0.05 µg L
-1

was observed in July of 2016 in the southern counties (the latter year exhibited exceptionally low
concentrations of domoic acid throughout the SCB). These differences between the northern and
southern counties of the SCB in the timing of toxic blooms of Pseudo-nitzschia, and the
magnitude and timing of the appearance of toxin in shellfish, indicate somewhat different factors
controlling toxic blooms of diatoms in these two sub-regions of the Bight.
1.3.3 Anatomy of a domoic acid event in the SCB: the role(s) of upwelling
A close relationship between coastal upwelling events along the California coast and the
appearance of phytoplankton blooms, specifically blooms of Pseudo-nitzschia and the
occurrence of domoic acid, has been documented for many years (Brzezinski and Washburn,
2011; Lange et al., 1994; Trainer et al., 2000). More than two decades ago Lange et al. (1994)
documented a correlation between the appearance of Pseudo-nitzschia off the coast of La Jolla
and the intrusion of cold, presumably upwelled water at the coast (albeit no major toxic episodes
were reported in that study). Blooms typically occurred between February and August, a
seasonality that is consistent with upwelling-favorable, down-coast winds peaking during winter-
spring (Hickey, 1992; Nezlin et al., 2012).
Nutrient delivery to coastal surface waters via upwelling is believed to be an important
stimulus for Pseudo-nitzschia growth and toxin production along the entire U.S. west coast
(Kudela et al., 2010), although other sources of nutrients may also contribute. McPhee-Shaw et
al. (2007) compared the importance of upwelling, storm runoff and diurnal motions for the
34
delivery of nutrients to the nearshore community in the Santa Barbara Channel. Seasonally,
storm runoff contributed most significantly to nutrient loading during winter, diurnal motions
contributed strongly during the summer, but upwelling was the dominant source of nutrients
between March and May (i.e. coinciding with the timing of most domoic acid events in the
SCB). Additionally, attention in recent years has focused on the potential for anthropogenic
nutrient sources to contribute to coastal phytoplankton blooms in the SCB (Nezlin et al., 2012).
As noted above (Section 1), nutrients discharged by large POTWs in the central Bight may
contribute as much as half of the annual nitrogen to coastal waters in the region, but those
nutrient discharges are not highly seasonal. Therefore, the recurrence of toxic blooms of Pseudo-
nitzschia during the spring in the SCB, as observed over the last 15 years, is consistent with
nutrient loading due to upwelling as a primary driver of toxic blooms of Pseudo-nitzschia in the
region. The timing of these events has been strongly linked to the timing of spring upwelling for
the southern counties of the SCB (Figure 1.4C), as well as the northern counties, although the
pattern has been less dramatic along the latter coasts (Figure 1.4B).
The classical pattern emerging from plankton studies within the region implicates nutrient
delivery into surface waters via upwelling during the spring when other conditions are
concurrently favorable for phytoplankton growth (Nezlin et al., 2012; Schnetzer et al., 2013;
Smith et al., 2018). Rapid decreases in surface water temperature at moored buoys or at pier
monitoring stations along the coast of the SCB have recorded the transport of deep, nutrient-rich
waters to the ocean surface (Figure 1.5). Pulses of upwelled water, followed by periods of wind
relaxation, typically result in rapid population growth of the endemic phytoplankton assemblage,
including Pseudo-nitzschia cells if present. Given sufficient ‘seed’ populations and time for
growth, the response of toxin-producing species of Pseudo-nitzschia can result in substantial
35
toxic events 1-2 weeks following the upwelling event (Figure 1.5B). Seubert et al. (2013) noted
this temporal progression at the Newport Beach Pier in Orange County, where a significant
relationship was observed between elevated Pseudo-nitzschia abundances two weeks after
upwelling events.
A general relationship between upwelling and outbreaks of domoic acid, as depicted in
Figure 1.5 during 2007, was also described by Schnetzer et al. (2013) and is further substantiated
by an examination of water temperature and salinity across our 15-year study period (Figure 1.6).
Patterns of the abundances of Pseudo-nitzschia and concentrations of domoic acid in the water
column plotted on T-S diagrams reveal that highest abundances and toxin concentrations were
consistently observed in cooler, saltier waters of this region (warmer colors in Figure 1.6),
characteristics consistent with upwelled water and elevated nutrient concentrations (see Figure 2
in (Seegers et al., 2015)). In particular, substantial toxin concentrations were only occasionally
observed at salinities <33, and never at temperatures >19˚C (Figure 1.6B).
The scenario described above (Figure 1.6) of nutrient loading of surface waters by upwelling
implies a significant amount of time between the upwelling event and the subsequent
development of a Pseudo-nitzschia bloom and appearance of toxin (typically 1-2 weeks
following the upwelling event). However, domoic acid can also appear in surface waters of the
central SCB during or immediately after an upwelling event, giving rise to an ‘instant’ domoic
acid event (Figure 1.7). Evidence presented by Seegers et al. (2015) implicated the uplifting of a
subsurface chlorophyll maximum containing toxic Pseudo-nitzschia cells into surface waters by
upwelling as a probable explanation for these observations. An Environmental Sample Processor
(Greenfield et al., 2006) deployed just above the subsurface chlorophyll maximum on the San
Pedro Shelf in that study exhibited higher abundances of Pseudo-nitzschia cells immediately
36
following shoaling of the subsurface chlorophyll feature (Figures 3-5 in (Seegers et al., 2015);
Figure 1.7A). Uplifting of the subsurface chlorophyll feature was documented using an
autonomous underwater vehicle, and surface manifestations of the uplifted chlorophyll layer
were apparent in Moderate Resolution Imaging Spectroradiometer (MODIS) images (Figures 4
and 5 in (Seegers et al., 2015); Figure 1.7B). Additionally, the authors reported that barnacles
that grew on the autonomous underwater vehicle during deployment contained significant
concentrations of domoic acid prior to the appearance of measurable concentrations of domoic
acid in surface water samples, implying that toxin contamination was attributable to
phytoplankton not present in surface water assemblages.
The scenario described above of an immediate or nearly immediate appearance of particulate
domoic acid at the time of an upwelling event constitutes an interesting corollary to the classical
1-2-week temporal progression from upwelling event to the appearance of domoic acid in
surface waters. Subsurface chlorophyll maxima and ‘thin layers’ are known to act as retention
areas in the water column for some phytoplankton species (Durham and Stocker, 2012; Ryan et
al., 2010; Velo-Suárez et al., 2008). The association of Pseudo-nitzschia populations with
subsurface chlorophyll maxima and thin layers have been documented along the California coast
and elsewhere (Rines et al., 2002; Rines et al., 2010; Seegers et al., 2015; Timmerman et al.,
2014), and has been hypothesized as a missing link explaining the existence of ‘cryptic blooms’;
that is, the appearance of toxin at higher trophic levels in the absence of the bloom in surface
waters (McManus et al., 2008). These subsurface features are cooler and contain higher
concentrations of nutrients than surface waters (conditions that may favor the growth of Pseudo-
nitzschia). Uplifting of these population into low nutrient, high light surface waters should
37
dramatically affect cellular physiology and may increase toxin production (Terseleer et al.,
2013).
We speculate that the impact of subsurface populations of toxic Pseudo-nitzschia on the
timing and magnitude of toxin appearance in surface waters following an upwelling event
depends not only on the amount of toxic Pseudo-nitzschia cells in the subsurface layer, but also
on the magnitude and duration of the upwelling event. Events that are too weak to bring the
subsurface Pseudo-nitzschia assemblage to the surface will be insufficient to affect conditions in
surface waters, while very strong upwelling events may result in surfacing and seaward
advection of the subsurface assemblage, limiting the nearshore manifestation or magnitude of an
‘instant’ domoic acid event.
1.4 Other factors affecting blooms in the Southern California Bight
General nutrient loading into surface waters as a consequence of coastal upwelling is
unquestionably a primary factor affecting Pseudo-nitzschia blooms and the production of domoic
acid in the Southern California Bight, but it is not the sole explanation for the observed pattern of
toxic events over the past 15 years. Pseudo-nitzschia blooms haven’t occurred in every year in
the region, nor have they occurred in response to every upwelling event. Additionally, when
Pseudo-nitzschia blooms have occurred, the blooms have not always resulted in domoic acid
production. That result is exemplified by the situation in 2013 and 2014, where peak spring
abundances of Pseudo-nitzschia in the central SCB were similar, yet maximal particulate domoic
acid concentrations differed by approximately two orders of magnitude (Smith et al., 2018). A
somewhat variable relationship between upwelling and domoic acid events is also supported by
studies that have not always reported strong correlations between domoic acid concentrations
and either abundances of Pseudo-nitzschia or total chlorophyll concentrations (Seubert et al.,
38
2013; Smith et al., 2018). Therefore, other factors appear to play secondary but important roles
in determining whether individual Pseudo-nitzschia blooms, or specific years, will result in toxic
events.
It is also apparent from our summary of the last 15 years that the details and magnitudes of
Pseudo-nitzschia blooms and domoic acid outbreaks in much of the SCB often have been
different from the situation in central and northern California as well as in the PNW. Rather than
a simple seasonal progression of blooms beginning in southern California and moving north as
spring progresses, conditions within the SCB have often been distinct from the fate of the
coastline to the north. For example, a massive domoic acid event in 2015 occurred along the west
coast of North America from central California to Alaska (McCabe et al., 2016). Only minor
concentrations of particulate toxin appeared in the northern counties of the SCB during that year,
while the central and southern Bight was virtually devoid of elevated concentrations of
particulate domoic acid. These varied outcomes presumably indicate subtle physical and
chemical differences in the coastal waters of southern California, resulting in differences in the
ability of these ecosystems to support the growth of Pseudo-nitzschia species and stimulate
domoic acid production by toxigenic species. In short, upwelling appears to be fundamentally
important, but other factors also contribute to toxic Pseudo-nitzschia blooms in the region.
1.4.1 Influence of timing, chemistry, and physics
Factors that might influence phytoplankton community activity, beyond the general nutrient
loading that occurs during upwelling events, include physical changes such as light and
temperature, and chemical modifications such as specific nutrient enrichment or depletion, or
changes in nutrient ratios. These changes may affect Pseudo-nitzschia dominance within the
phytoplankton, species composition within the assemblage, and the induction of toxin
39
production. These factors presumably act synergistically, a factor that has complicated our
process of attributing toxic events to any specific factor(s).
1.4.2 Water temperature
Temperature appears to be an important factor constraining blooms of Pseudo-nitzschia and
toxin production in the SCB (Figure 1.6, Supplemental Figure 1.3). Upwelling in the SCB can
decrease surface water temperatures in the coastal ocean to 13-14˚C, a condition that may favor
the growth of some Pseudo-nitzschia species (Lelong et al., 2012). Interestingly, high
abundances of Pseudo-nitzschia have not been reported in the region at temperatures exceeding
20˚C, and virtually no substantive values of particulate domoic acid in plankton samples have
been recorded above ≈19˚C (Figure 1.6, and Supplemental Figures 3A and 3B, respectively).
These findings imply that the timing (and/or magnitude) of seasonal upwelling events (i.e.
nutrient loading of surface waters), and specifically the surface water temperatures attained
during and immediately following these events, may strongly influence whether Pseudo-
nitzschia species will dominate the phytoplankton assemblage and produce toxin. The timing of
seasonal upwelling in northern California was examined by Schwing et al. (2006), whose results
are in accordance with this speculation. The authors noted a 2-3 month delay in the spring
upwelling season in northern California during 2005 relative to other years (no information was
provided for southern California). Concurrently, abundances of Pseudo-nitzschia and particulate
domoic acid concentrations were very low in the central and southern SCB during spring 2005,
although values in the previous year (2004) and following two years (2006-2007) were
substantial (Schnetzer et al., 2013; Schnetzer et al., 2007). We speculate that the delayed
seasonal upwelling during 2005, and warmer water temperatures at the time of the onset of
40
upwelling, may explain the lack of a significant domoic acid event in the central and southern
SCB during that year.
The empirical observation that water temperature above 19˚C did not result in Pseudo-
nitzschia dominance or domoic acid production in the SCB (Figure 1.6, Supplemental Figure
1.3B) implies that nutrient loading, by itself, might not necessarily result in toxic events if the
receiving surface waters are too warm. This speculation is in agreement with observations during
2015, an anomalously warm year that witnessed a massive domoic acid event extending from
central California to Alaska but little to no toxin produced in the SCB (McCabe et al., 2016).
It is also in accordance with the results of two recent, large-scale discharges of sewage
effluent into nearshore waters. The Orange County Sanitation District (OCSD) off Newport
Beach on the San Pedro Shelf, and the Hyperion Treatment Plant (HTP) of the City of Los
Angeles off El Segundo in Santa Monica Bay, conducted diversions from their offshore outfall
pipes (discharging below the euphotic zone ≈8 km from shore) to pipes ≈1.6 km from shore to
enact repairs on the longer pipes during the fall of 2012 and 2015, respectively. Massive nutrient
loading into nearshore surface waters resulting from these discharges (~12 x10
6
m
3
of treated
wastewater discharged during the OCSD diversion and ~39x10
7
m
3
of treated wastewater
discharged during the HTP diversion, containing nutrient nitrogen at concentrations
approximately three orders of magnitude above ambient concentrations) was expected to result in
dramatic responses of the phytoplankton community and potentially the development of HABs
(Howard et al., 2017; J. Smith unpublished data).
In contrast to anticipated outcomes, the OCSD diversion resulted in only a modest increase in
phytoplankton biomass, where Pseudo-nitzschia was not a significant component of the
community and domoic acid was near or below detection throughout the 3-week diversion
41
(Caron et al., 2017). The 6-week HTP diversion resulted in three taxonomically distinct and
substantive phytoplankton blooms (diatoms, euglenids, raphidophytes) within Santa Monica Bay,
but none of them contained significant abundances of Pseudo-nitzschia or measurable
concentrations of domoic acid (J. Smith unpublished data). Cruises conducted following the
2015 diversion (November 2015), however, did reveal some low concentrations of toxin and
increased abundances of Pseudo-nitzschia cells in the plankton – potentially a result of seasonal
cooling of surface water temperature. A similar but shorter diversion (<3 days) from HTP during
fall 2006 resulted in a significant stimulation of dinoflagellates within the phytoplankton
assemblage, but no significant response of the Pseudo-nitzschia assemblage (Reifel et al., 2013).
We speculate that these surprising results were, at least in part, a consequence of effluent release
during a season not conducive to growth and toxin production by potentially toxic Pseudo-
nitzschia species in the Southern California Bight (presumably due to supraoptimal temperatures
of >19˚C).
1.4.3 River discharge and composition
Much of the river discharge to the coastal ocean in the SCB is seasonal and enters the ocean
through a relatively small number of large rivers. The potential impacts of these discharges are
therefore somewhat localized within the Bight and their contributions to blooms and domoic acid
are complicated by the fact that the season of strongest river discharge typically co-occurs with
the season of upwelling events. Additionally, interannual variability in total discharge volume
can be significant, and that variability has been very high since 2003 (>20-fold; gray bars in
Figure 1.8).
River discharge in central California has been proposed as a factor that may play a
stimulatory role in Pseudo-nitzschia blooms and domoic acid production due to nitrogenous
42
compounds contained in agricultural runoff that are a significant component of the discharge in
that region (Kudela et al., 2008). Such anthropogenic point sources of nutrients comprise a
significant amount of the terrestrial nutrient loads to the SCB; >90% of total nitrogen and >75%
total phosphorus (Sengupta et al., 2013). However, a direct relationship between river discharge
and Pseudo-nitzschia growth or toxin production is much less clear for the SCB (Schnetzer et al.,
2013), perhaps due to the fact that rivers in the Bight receive runoff from a wide spectrum of
land use ranging from agriculture in the north to highly urbanized and industrialized sectors in
the central Bight.
Howard et al. (2014) characterized river discharge in the SCB as a significant component of
total nitrogen delivery to the coastal ocean, but minor in comparison to nitrogen delivered via
upwelling, or discharge from large POTWs in the region. Rivers were, however, a major
percentage of the organic nitrogen compounds entering the coastal ocean. Nevertheless, data
compiled for the period 2003-2015 comparing discharge from the major river systems in the
southern counties of the SCB with maximal or average concentrations of domoic acid in coastal
waters near those discharges have not revealed a relationship (Figure 1.8). In fact, high average
and maximal concentrations of particulate domoic acid have been observed during ‘wet’ years
(e.g. 2011), ‘dry’ years (e.g. 2007, 2013), and years with median river discharge (e.g. 2003,
2006, 2008). The two years with the largest river discharges depicted in Figure 1.8 (2005, 2010)
exhibited very low concentrations of domoic acid in the SCB, while 2017 (a very wet year)
witnessed a large domoic acid event in the Bight (total discharge data not yet available for 2017).
1.4.4 Macro/micronutrients, nutrient depletion, and nutrient ratios
Generalized relationships between nutrient availability and toxic blooms in the SCB, beyond
the relationships to upwelling and temperature noted above, have not been dramatic when
43
examined across the entire available data set (~2330 data points) (Supplemental Figure 1.4) or
for the spring months of March, April and May (~1900 data points, data not shown) when
Pseudo-nitzschia blooms occur most regularly. These loose relationships tend to fit ‘standard’
expectations reported in the literature for the stimulation of Pseudo-nitzschia blooms and domoic
acid events (Lelong et al., 2012). Nutrient limitation during bloom formation, in particular
silicate and/or phosphorus limitation, or low ratios between those elements and other nutrient
elements (e.g. low Si:N) have yielded the most consistent correlations with domoic acid in the
Bight (Anderson et al., 2009; Schnetzer et al., 2013; Schnetzer et al., 2007; Smith et al., 2018).
Similar relationships have been observed for Pseudo-nitzschia blooms north of the SCB in
Monterey Bay (Lane et al., 2009; Ryan et al., 2017). These correlations confirm a well-
documented effect of nutrient status of the water on toxin production as a Pseudo-nitzschia
bloom progresses, although this effect is presumably secondary to the factors described above.
Less-well-characterized relationships between water chemistry and Pseudo-nitzschia species
composition and/or toxin production remain to be examined in the SCB. The form of nitrogen
(NH
4
+
, NO
3
-
and urea) has been shown to affect growth and toxicity of Pseudo-nitzschia species
(Howard et al., 2007; Kudela et al., 2008; Thessen et al., 2009), but to date, this effect is largely
unexamined in field studies conducted in the Bight. Synergistic effects are also poorly
understood. For example, high CO
2
, phosphate limitation, and silicate limitation have been
shown to act synergistically to increase the toxicity of some Pseudo-nitzschia species (Sun et al.,
2011; Tatters et al., 2012). These complex interactions are exceedingly difficult to identify in
field datasets, but they may be fundamental in determining which Pseudo-nitzschia species will
dominate a bloom or whether toxin production will be stimulated among toxigenic species.
Likewise, there is little information with respect to how domoic acid production by Pseudo-
44
nitzschia species in the SCB may be affected by trace metal or vitamin status, although
correlations in laboratory cultures and some field studies have indicated that they can play a role
(reviewed in Lelong et al., 2012).
1.4.5 Long-term relationships and drivers
Climatic variability and its effects on oceanographic conditions unquestionably play a role in
the year-to-year variability of coastal phytoplankton blooms, Pseudo-nitzschia abundances and
maximal particulate domoic acid concentrations in the SCB. Climate indices such as the Pacific
Decadal Oscillation (PDO) (Mantua and Hare, 2002; Mantua et al., 1997), North Pacific Gyre
Oscillation (NPGO) (Di Lorenzo et al., 2008) and the Multivariate El Niño Southern Oscillation
(ENSO) Index (MEI: indicative of ENSO dynamics) (Wolter and Timlin, 1993, 1998) have been
correlated to low-frequency patterns in oceanographic conditions which in turn impact biological
communities, such as zooplankton abundances and fish stocks, in the northeast Pacific
(Lavaniegos and Ohman, 2007; Lynn et al., 1998; McGowan et al., 1998). These large-scale
climatic patterns vary on timescales ranging from months and years (ENSO) to decades (NPGO
and PDO) (Alexander, 2010).
ENSO can be linked to the decadal dynamics of the PDO and NPGO, and the manifestations
of the three climatic patterns result in definable changes in the oceanography of the California
Current (Di Lorenzo et al., 2013). The PDO is the first dominant mode of variation in sea surface
temperature anomalies (SSTa) and sea surface height anomalies (SSHa) in the northeast Pacific.
The positive phase of the PDO index generally results in increased biological productivity along
the Alaskan coast and muted productivity along the more southern regions of the North
American west coast, including parts of California (Mantua and Hare, 2002; Mantua et al.,
1997). The negative phase of the PDO is marked by the opposite trend with increased biological
45
productivity along the North American west coast. In either phase, the PDO generally exerts a
greater influence on regions north of 38°N (approximately the latitude of San Francisco,
California) (Chhak and Di Lorenzo, 2007; King et al., 2011).
The NPGO is the second dominant mode of SSHa variability in the northeast Pacific and also
captures the second mode of north Pacific SSTa variations. Prominent low-frequency changes in
salinity, nutrients, sea level and chlorophyll across the Pacific region have been attributed to
phase changes in the NPGO index (Di Lorenzo et al., 2008), particularly in regions south of
38°N (Chhak and Di Lorenzo, 2007; King et al., 2011). The positive phase of NPGO represents
the strengthening of the geostrophic circulation of the North Pacific Gyre and manifests as
increased southward transport of the California Current System (CCS) and intensification of
upwelling favorable wind patterns (Di Lorenzo et al., 2008).
ENSO activity in the equatorial Pacific, as indicated by MEI (Wolter and Timlin, 1993,
1998) is characterized by variations between El Niño warm phases and La Niña cold phases
(indicated by positive and negative MEI phases, respectively). El Niño events have been shown
to impact the CCS, generally resulting in weaker coastal upwelling and fresher, warmer and
shallower source waters; conversely, La Niña typically manifests with the opposite trend (Jacox
et al., 2015).
Long-term trends in particulate domoic acid concentrations from our data set were examined
in relation to climatic indices, demonstrating significant relationships with NPGO and MEI but
not PDO (Table 1.1). Particulate domoic acid concentrations above the limit of detection (0.01
µg L
-1
or 0.02 µg L
-1
, depending on data source) were matched by month to respective index
values, and then separated into two groups based on whether the respective climatic index was in
the positive or negative phase. The same analysis was also conducted with a 1-month lag (the
46
index and toxin concentration in the water one month later), assuming that bloom development
and toxin production require time to respond to climatic shifts. A bulk comparison was
conducted between the two groups of particulate domoic acid concentrations using the Mann-
Whitney Rank Sum Test. Significance was determined at p < 0.05 (Table 1.1).
The PDO showed no significant relationship to the median concentration of particulate
domoic acid observed during the negative or positive phase, with or without a time lag. This
implies, to the extent that the available data can determine, that PDO has not exerted a clear
influence on overall particulate domoic acid concentrations in the SCB during the past decade
and a half. This result may reflect the fact that previous studies have noted that the PDO exerts a
greater influence on regions north of 38°N; i.e., north of central California (Chhak and Di
Lorenzo, 2007; King et al., 2011), and that the influence of the PDO within the SCB is
moderated or subdued by other factors.
Median particulate domoic acid concentrations were higher during the negative phase of
NPGO than in the positive phase with both lagged and un-lagged data (Table 1.1), indicating that
the negative phase of the NPGO may enhance toxigenic Pseudo-nitzschia events in the SCB.
This result is surprising given that the positive phase of the NPGO is characterized by conditions
that favor coastal upwelling (Di Lorenzo et al., 2008), which has generally been shown to play
an important role in toxigenic bloom development in the Bight (Figure 1.6). The majority of data
points in the present analysis, however, were collected during the positive phase of the NPGO
(Table 1.1), potentially skewing our analysis.
ENSO (MEI) showed a significant difference between median concentrations of particulate
domoic acid measured during the negative and positive phases, with higher median particulate
domoic acid concentrations occurring during the negative phase of the MEI (Table 1.1). The
47
negative phase of MEI is associated with decreased sea surface temperatures (SST) which, as
noted above (Figure 1.6; Supplemental Figure 1.3), appear to favor toxigenic Pseudo-nitzschia
blooms in the SCB.
The relationships noted above between particulate domoic acid within the SCB and large-
scale climate variability indices are somewhat at odds with the general patterns reported in the
literature pertaining to toxic events along the U.S. west coast. Admittedly, these patterns are
difficult to assess from a dataset that is 15 years long, particularly for climate patterns that span
~10 years. However, another factor that appears significant is that previous reports have largely
focused on the coastline from Santa Barbara county northward, and have not specifically
addressed the southern regions of the SCB, as in our analysis. McCabe et al. (2016) and
McKibben et al. (2017) suggested that domoic acid events (i.e. shellfish contamination)
occurring from central California to Washington State were related to warm phases of the PDO
and El Niño periods. We did not detect a significant relationship between particulate domoic acid
concentrations and PDO in our dataset for the SCB; rather, our results suggest La Niña periods
are related to higher particulate domoic acid concentrations in the Bight (Table 1.1). Sekula-
Wood et al., (2011) reported a relationship between the positive phase of the NPGO and elevated
domoic acid concentrations measured from sediment trap samples from the Santa Barbara Basin
at the northern end of the SCB from 1993 to 2008. Yet our analysis, which included data from
across the entire Southern California Bight, detected the opposite trend between 2003 and 2017.
We surmise that toxic blooms within the more southern regions of the SCB have been influenced
by somewhat different dynamics than those acting in the northern counties of the Bight, and that
the northern counties appear to be more in agreement with the central and northern Californian
coast. This speculation seems to be consistent with observed differences in the magnitude of
48
spring upwelling that can exist between the northern sector of the SCB relative to the central and
southern sectors (Supplemental Figure 1.5). Additionally, our speculation is supported by
differences in the magnitude and frequency of domoic acid in shellfish of the northern vs.
southern counties of California observed in our synthesis (Figure 1.2).
It also appears that during the past 15 years, surface water temperature has been an
overriding factor controlling the production of domoic acid in the SCB. We observed a strong
relationship between surface water temperature ≤19˚C and domoic acid, as noted above (Section
4.1.1; Figure 1.6, Supplemental Figure 1.3). In accordance with that finding, an analysis of two
SSTa time series from Newport Pier (located in Orange County) and Stearns Wharf (located in
Santa Barbara County) indicate that elevated domoic acid concentrations generally do not occur
during periods of sustained positive temperature anomalies (Figure 1.9A, C). Conversely,
McCabe et al. (2016) postulated that the large toxigenic Pseudo-nitzschia bloom that occurred
from central California northward during 2015 was related to positive temperature anomalies.
However, these anomalies resulted in SSTs of ≤19˚C throughout the region affected by the toxic
bloom (see Figure 2 in McCabe et al. 2016), while surface water temperatures during this period
in southern California were ≥19˚C (Figure 1.9B, D). Generally, mean absolute SSTs are higher
in the Bight (particularly in the southern regions of the SCB), than along the west coast to the
north of the SCB. These observations are consistent with the conclusion that processes that
increase temperatures along the U.S. west coast (such as El Niño) may stimulate the growth of
Pseudo-nitzschia in more northern regions, but appear to have a negative effect on Pseudo-
nitzschia populations in the SCB. Our results suggest that temperatures above 19˚C in the Bight
will suppress the growth and buildup of toxigenic Pseudo-nitzschia cell populations, thereby also
49
resulting in lower particulate toxin concentrations at higher temperatures. The effect of
temperature on toxin production on a per cell basis, however, is less clear.
Beyond latitudinal differences along the coast and their effects on water temperature, two
other features may help explain differences in the response of the phytoplankton community
within the SCB from communities to the north. Firstly, the orientation of the coastline of the
Southern California Bight to prevailing wind patterns along the coast is different than the
coastline to the north, affecting the magnitude of upwelling events and heterogeneity associated
with their geographic distribution within the Bight. Secondly, the Channel Islands, which are
located throughout the Bight, result in a complicated pattern of coastal circulation (Figure 1.1)
that may temper the applicability of relationships derived from observations farther north along
the coast. The islands also impact retention time, which has shown to be important by Nezlin et
al. (2012).
1.4.6 Implications for modeling and prediction
A long-term goal of the work conducted in the SCB is the development of an operational
forecasting model for toxigenic Pseudo-nitzschia bloom events. The development of operational
HAB models for predicting blooms, including toxigenic Pseudo-nitzschia, is a central objective
of NOAA’s Ecological Forecasting Roadmap (Anderson et al., 2016). At the time of this writing,
operational models of this type exist for Karenia brevis blooms in the Gulf of Mexico (Stumpf et
al., 2009), and for cyanobacterial blooms in Lake Erie (Wynne et al., 2010). These efforts are the
joint work of NOAA National Ocean Service’s (NOS) National Centers for Coastal Ocean
Sciences (NCCOS), government agencies, researchers, and regional associations of the U.S.
Integrated Ocean Observing System (IOOS) to develop NOAA’s HAB Operation Forecasting
System (HAB-OFS; https://tidesandcurrents.noaa.gov/hab/). Pilot HAB forecast models are
50
being developed with the goal of becoming operational for Alexandrium in the Gulf of Maine
(He et al., 2008; McGillicuddy et al., 2005; Stock et al., 2005), Alexandrium and Pseudo-
nitzschia in the PNW (Trainer and Suddleson, 2005), and Pseudo-nitzschia along the California
coast (Anderson et al., 2016).
Predictive modeling of Pseudo-nitzschia related HAB events requires the determination of
pertinent variables and their measurement on appropriate temporal and spatial scales (Jochens et
al., 2010). There are currently few environmental factors that have been unequivocally linked to
increases in Pseudo-nitzschia cell abundances or the initiation of domoic acid production in situ.
As noted above, the conditions related to increases in Pseudo-nitzschia abundances can differ
from the conditions related to domoic acid production, creating an additional challenge. Several
studies have identified variables that might be used to help model toxigenic Pseudo-nitzschia
blooms (Anderson et al., 2011; Anderson et al., 2016; Anderson et al., 2009; Blum et al., 2006;
Lane et al., 2009). Limiting macronutrient concentrations, most often silicate, have emerged as
an important factor in DA-producing blooms from several of these studies (Anderson et al.,
2011; Anderson et al., 2009; Blum et al., 2006; Lane et al., 2009). Another challenge to
predictive modeling is the need for substantial and sustained data input into the model. Remotely
sensed information, particularly satellite imagery, has provided the most extensive temporal and
spatial coverage, making it useful for nowcast and forecast models. A major challenge to this
approach, however, is the lack of a novel optical signal for domoic acid or Pseudo-nitzschia
abundances from the currently available multi-spectral ocean color satellite sensors (Anderson et
al., 2016). Approaches using chlorophyll anomalies from satellite observations have yet to be
successfully applied to predict Pseudo-nitzschia or domoic acid events with high accuracy, as
51
they have with other forecast models such as those for Karenia brevis blooms (Stumpf et al.,
2009).
The majority of Pseudo-nitzschia and domoic acid modeling studies in the SCB have focused
on the Santa Barbara Channel region to central California. Anderson (2009) utilized a stepwise
linear regression approach to identify in situ biological and physiochemical factors, as well as
remotely sensed data, that contributed to Pseudo-nitzschia blooms and DA over a 1.5-year
period. Remote sensing reflectance ratio (R
rs
) (510/555), Si:P, SST and sea surface salinity (SSS)
were the strongest predictors of particulate domoic acid concentrations. The model performed
well at estimating the presence or absence of particulate domoic acid (e.g. thresholds), but was
less skilled at estimating the absolute concentration of the toxin and, overall, performed better at
predicting Pseudo-nitzschia cell thresholds than domoic acid thresholds. Unsurprisingly, the
conditions related to elevated Pseudo-nitzschia abundances differed from those related to
elevated particulate domoic acid. Conditions related to Pseudo-nitzschia abundances above a
bloom threshold were found to be R
rs
(412/555), ln(Si:N), R
rs
(555), particulate absorption
(A
p
)(490), and R
rs
(510/555).
Anderson (2011) expanded the work conducted by Anderson et al. (2009) and employed
satellite-derived ocean color data and the Regional Ocean Modeling System (ROMS)
(Shcehpetkin and McWilliams, 2005) to estimate circulation patterns, sea surface temperature
and sea surface salinity, and an updated empirical HAB model that utilized a generalized linear
model approach with a larger data set. A ‘full model’ that included all available data and
‘remote-sensing’ model that included only variables from remote platforms were produced. The
predictors of the ‘full model’ generally agreed with previous Pseudo-nitzschia and DA modeling
studies (Anderson et al., 2009; Lane et al., 2009), identifying R
rs
(510/555), Si:N, Si:P, SST and
52
SSS as significant predictors of DA. The ‘full model’ demonstrated greater predictive skill than
the ‘remote-sensing’ model; however, nowcasts and forecasts are currently only possible using
remotely sensed data due to its higher temporal and spatial coverage compared to in situ
measurements.
The California Harmful Algal Risk Mapping (C-HARM) system (Anderson et al., 2016),
(http://www.cencoos.org/data/models/habs) is a pre-operational model that provides a risk map
of particulate domoic acid, cellular domoic acid, and Pseudo-nitzschia. C-HARM has been in
operation since February 2014 and is built upon the efforts of Anderson et al. (2011; 2009). The
system utilizes ROMS to estimate circulation patterns, sea surface temperature, and salinity,
MODIS Aqua (MODISA) to derive ocean color data, and the previously developed empirical
models for toxigenic Pseudo-nitzschia blooms noted above. This model also utilizes Data
Interpolating Empirical Orthogonal Function (DINEOF), a data interpolating technique, to fill
gaps in satellite coverage to enhance the nowcast and forecast abilities of the model. Currently,
C-HARM has been shown to be more skilled at the estimation of particulate domoic acid than
Pseudo-nitzschia cell abundance thresholds. Anderson et al. (2016) reported that the model
correctly predicted more than 50% of the domoic acid events observed above a designated event
threshold at the Santa Cruz Municipal Wharf as well as some of the shore stations maintained by
California Harmful Algal Bloom Monitoring and Alert Program (CalHABMAP) and Southern
California Coastal Ocean Observing System (SCCOOS) during the validation study. The bulk of
the validation study was conducted with data collected from central California at Santa Cruz
Municipal Wharf in central California and in Santa Barbara County of the SCB at Stearns Wharf.
Lower predictive ability was reported at some of the shore stations, particularly at the shore
53
stations in San Diego County (Scripps Pier) and in San Luis Obispo county (Cal Poly Pier),
again indicating potential regional disconnections between sites in the SCB as noted above.
The approach of merging data from various platforms in C-HARM has proven useful, and
model skill will undoubtedly improve as the data coverage and resolution from remote sensing
and ROMS platforms increases. Additionally, the development of a regional biogeochemical
model capable of estimating nutrient concentrations would likely enhance the skill of C-HARM
as indicated by the results of Anderson et al. (2011; 2009), where models that included nutrient
data were generally more skilled than those with remotely sensed data alone.
1.5 Food web consequences of toxic blooms in the Southern California Bight
The consequences of domoic acid events along the U.S. west coast, and within the SCB, have
stimulated awareness of the risk that the toxin poses to human health (and safeguards to prevent
exposure). These events have resulted in mass animal mortality events and losses in fishery
revenue due to contamination of pelagic and benthic food webs. Documentation of domoic acid
within coastal marine food webs, and animal mortalities attributable to the toxin in the SCB
followed the first reports of mass mortality events from central California in the late 1990s.
Most studies of marine animal poisoning have focused primarily on the California sea lion
population (Zalophus californianus) in central California north of the SCB, but have also
included events within the Bight (Bargu et al., 2012; Bargu et al., 2010; Torres de la Riva et al.,
2009). A substantial body of work on sea lions followed the mass mortality event of 1998 (north
of the Bight) and events in the SCB during 2002, 2006 and 2007. These studies have brought
attention to the role of domoic acid in sea lion strandings along the west coast (Scholin et al.,
2000), and provided possible explanations for mass mortality events of other species. Since that
time, domoic acid poisoning in sea lions has been linked to premature parturition and abortion,
54
disruption of hippocampal-thalamic brain networks and other neurological problems (Cook et al.,
2015; Goldstein et al., 2008; Goldstein et al., 2009; Silvagni et al., 2005). Significant mortality
events of seabirds attributable to domoic acid have also been documented in the region,
including brown pelicans (Pelecanus occidentalis) and other birds off Baja (Sierra Beltrán et al.,
1997) and central California (Fritz et al., 1992; Work et al., 1993).
Losses to marine animal populations attributable to domoic acid have been significant.
During the period of 2001-2009, nearly 27,000 marine mammal strandings were recorded along
the coast of California (http://www.nmfs.noaa.gov/pr/health/prescott/: ≈94% pinnipeds, ≈6%
cetaceans; 40% of animals were dead). In recent years, marine animal deaths have occurred with
considerable frequency
(http://www.whoi.edu/redtide/page.do?pid=18103&tid=542&cid=47892&c=3). Strandings have
many causes, including injury, disease, even unusual oceanographic conditions (Melin et al.,
2010) but published reports also indicate a strong link between some of these mass mortality
events and algal toxins (particularly domoic acid) in marine food webs (Gulland, 2006). Other
marine mammals along the California coast for which domoic acid poisoning has been
demonstrated include Pacific harbor seals (McHuron et al., 2013), northern fur seals (Lefebvre et
al., 2010) and southern sea otters (Kreuder et al., 2003). As a consequence of this work,
biotoxins are increasingly recognized as a major cause of mass mortality events for marine
mammal populations, and the overall number of events in the U.S. southwest region has
increased markedly during the past few decades (Gulland, 2006).
Data collected during mortality events in 2006 and 2007 documented the pattern and impact
that toxic Pseudo-nitzschia blooms have on marine animal populations in the SCB (Figure 1.10).
Both years witnessed massive toxic blooms of domoic acid on the San Pedro Shelf near the
55
mouth of the Los Angeles Harbor in the central Southern California Bight. Maximal
concentrations of particulate domoic acid of approximately 15 and 25 µg L
-1
during 2006 and
2007, respectively, were observed in the particulate domoic acid fraction (Figure 1.10A, B).
Concentrations of domoic acid averaged across 20 sampling stations on the Shelf were also high,
and the timing of the appearance of particulate toxin coincided with the appearance of domoic
acid in marine mammals and seabirds stranding at that time (Figure 1.10C, D). Peaks in the
number of animals testing positive for domoic acid co-occurred with the time of seasonal peaks
in the number of stranding animals, and at or just before the time of peak particulate toxin
concentrations were observed (Figure 1.10C, D). Species affected in the 2006-2007 mortality
events included several bird species of concern in California, including common loon, double-
crested cormorant, rhinoceros auklet and California gull.
Studies beyond charismatic macrofauna along the U.S. west coast have documented that
domoic acid is pervasive throughout the marine food web during toxic events (Lefebvre et al.,
2002). The accumulation of domoic acid in sardines, anchovies, and krill during toxic blooms is
well documented because these species constitute ‘vectors’ for the trophic transfer to species that
prey on them (Bargu et al., 2002; Costa and Garrido, 2004; Lefebvre et al., 1999). However,
contamination and/or death of a wide variety of species has been demonstrated including several
pelagic and benthic fish, Humboldt squid and at least one Minke whale in the SCB (Busse et al.,
2006; Fire et al., 2010; Mazzillo et al., 2011).
Contamination of benthic ecosystems and biological communities with domoic acid due to
the potential for rapid sinking of toxic diatom cells and other particulate material in the SCB has
also been demonstrated (Busse et al., 2006; Powell et al., 2002; Schnetzer et al., 2007; Sekula-
Wood et al., 2009). Recent years have witnessed numerous closures of razor clam (and other
56
bivalve mollusks), rock crab and Dungeness crab fisheries. The considerable magnitude and
consequences of the contamination of benthic food webs were well-documented during the
extensive domoic acid event along much of the U.S. west coast north of the SCB in 2015
(McCabe et al., 2016).
Continuing problems for marine animal health in the Southern California Bight include the
re-occurrence of toxic Pseudo-nitzschia blooms (and resulting animal mortality events) such as
the event that emerged in spring 2017 (http://www.ocregister.com/2017/04/10/are-toxic-algae-
blooms-sickening-a-record-number-of-sea-lions/). Emerging issues include the potential for
multiple toxins and other stressors to impact animal populations (Fire et al., 2010; Gulland,
2007), the transport of freshwater toxins from freshwater environments where they are produced
into marine food webs (e.g. otter deaths in central California; (Miller et al., 2010)), and exposure
to several algal and/or cyanobacterial toxins in an environment (Tatters et al., 2017).
1.6 Conclusions and future efforts
Much attention has been garnered recently regarding ocean warming and its realized and
anticipated impact on the global distribution of HABs. Concern within the scientific community
is particularly acute for the development of freshwater cyanobacterial blooms and their
associated toxins (Paerl, 2014; Paerl and Huisman, 2009; Paerl and Paul, 2012), but similar
concerns surround the intensity and frequency of well-documented marine HABs (Gobler et al.,
2017). These concerns include blooms and events along the west coast of North America
produced by species of Pseudo-nitzschia (McCabe et al., 2016; McKibben et al., 2017),
Alexandrium and Dinophysis (Jester et al., 2009), as well as HAB issues only recently
documented in the region (Caron et al., 2010; Howard et al., 2008; Howard et al., 2012; Jessup et
al., 2009; Reifel et al., 2013; J. Smith unpublished data). McCabe et al. (2016), for example,
57
speculated that the massive domoic acid outbreak along the west coast from central California to
Alaska was in part a consequence of a northern expansion of the range of P. australis enabled by
anomalously warm ocean temperature throughout the region in that year.
Paradoxically, while ocean warming may expand the northern distributions of some HAB
species, we speculate that ocean warming may actually reduce the occurrence of domoic acid in
the Southern California Bight, based on information summarized here. Overall, the last 15 years
have witnessed recurrent toxic events, but the recent exceptional drought years in the U.S.
southwest (2014-2016) witnessed very low concentrations of particulate domoic acid in the Bight
(Figure 1.3). Moreover, the SCB experienced extremely low domoic acid concentrations during
2015, completely anomalous to the massive toxic event that occurred along the entire North
American coast north of the Bight. We speculate that rising water temperatures may shrink the
seasonal ‘window of opportunity’ for Pseudo-nitzschia species (i.e. the period of cooler surface
water temperatures with sufficient light for population growth; Figure 1.4C), particularly in the
southern SCB, thereby reducing their competitive ability. Future domoic acid events are
anticipated in the Bight due to year-to-year and decadal-scale climatic variability, but a general
trend towards higher surface water temperatures in the region may act to limit or even prevent
these toxic events.
The speculation above must be tempered, of course, by the possibility that warm-adapted,
toxigenic species of Pseudo-nitzschia may eventually dominate in the region. Our dataset
strongly indicated blooms of Pseudo-nitzschia and domoic acid production did not occur at
surface water temperatures above 19˚C (Figure 1.6, Supplemental Figure 1.3). However, there is
no a priori reason to believe that warm-adapted toxigenic species of Pseudo-nitzschia will not
become established in the region as ocean water warms, and continue to cause domoic acid
58
events in future years. Indeed, Zhu et al. (2017) documented a P. australis strain isolated from
the SCB that showed increased toxin production and growth at 23˚C in culture, suggesting that
some toxigenic strains of Pseudo-nitzschia exist in the region.
Perhaps more significantly, the role of subsurface chlorophyll maxima (including ‘thin
layers’) as reservoirs of toxic populations of Pseudo-nitzschia is poorly understood. Evidence
exist for the presence of toxic cells in subsurface layers within the SCB, but there is only cursory
information on the extent to which these cells/toxins might seed surface blooms along the coast,
explain the rapid emergence of toxic events due to uplifting of these layers during upwelling
events, or contribute to animal strandings/mortalities when no surface manifestation of domoic
acid is apparent (i.e. ‘cryptic blooms’). Establishing the significance of these phenomena should
be a topic for future study and clarification.
Additionally, our understanding is poor with respect to blooms originating or taking place
offshore within the Southern California Bight. The contribution of offshore blooms in the SCB to
animal strandings and mortalities events is also poorly documented at this time. The potential for
onshore advection of offshore toxic blooms to contribute to domoic acid in surface waters along
the coast is understudied in large part because offshore monitoring and surveillance is generally
sparse and/or ad hoc. Additional research is needed to gain a better understanding of the
potentially important impact of offshore Pseudo-nitzschia dynamics and the level of connectivity
to the onshore regions of the Bight.
59
1.7 Chapter One Figures and Tables

Figure 1.1: Map of the Southern California Bight (Point Conception to San Diego) indicating
the general circulation pattern within the region. Modified from Hickey (1992). Inset shows the
location of the Bight along the California coast.
60

Figure 1.2: Scatter plots of domoic acid concentrations measured in shellfish tissue (mussels
and oysters) over the period of 2003 to 2016 in each coastal county of California (total number
of samples, n = 4528). Individual counties are color-coded on the map, and correspond to the
same colors on each scatter plot. Panel (A) shows samples collected in northern California
61
(n=620), (B) shows samples collected in central California (n=1966), (C) shows samples
collected in the northern counties of the Southern California Bight (n=1335), and (D) shows
samples collected in the southern counties of Southern California Bight (n=607). Data
summarized from California Department of Public Health Center for Environmental Health
(2014).
62

Figure 1.3: Summary of ≈4,500 particulate domoic acid measurements for plankton samples
collected and analyzed during the last 15 years from the Southern California Bight (see
Supplemental Table 1.2 for complete dataset of samples that yielded values of domoic acid
above the methodological limit of detection). Symbols indicate samples collected shipboard at
the surface, defined as <2 meters depth, (open circles) or subsurface, defined as a depth of >2
meters depth, (filled circles), or by bucket from pier stations (inverted filled triangles).

63

Figure 1.4: Average and maximal concentrations of domoic acid in shellfish tissue. Grey bars
show the average domoic acid concentrations in shellfish tissue samples in each month for the
period of 2003 to 2016. The black line indicates the maximal domoic acid concentration
measured in each month during the same period. (A) data from all coastal counties in California;
64
(B) data from the northern counties of the Southern California Bight only (Santa Barbara and
Ventura); (C) data from the southern counties of the Southern California Bight only (Los
Angeles, Orange and San Diego). Arrows indicate the month in which the maximal particulate
domoic acid concentrations were observed during each year, with the color of the arrow
indicating the year. Shellfish data obtained from California Department of Public Health Center
for Environmental Health (2014).
65

Figure 1.5: Typical relationship between the timing of spring coastal upwelling, resulting in a
decrease in temperature of surface waters at the coastline, followed by subsequent population
growth and toxin production by Pseudo-nitzschia species in the central Southern California
Bight. Temperature information (A) was obtained from the NOAA weather buoy (Station 46222
– San Pedro, California (092), 33.618˚N, 118.317˚W). Inset (B) shows the distribution of
particulate domoic acid in the plankton community at the surface on 27 April 2007,
approximately one week after the minimum in water temperature. LA hb indicates the Los
Angeles harbor, black dots show the shipboard sampling stations, the legend indicates particulate
domoic acid concentrations. Note that inset (B) also appears in Figure 1.10, which demonstrates
the temporal relationship between particulate domoic acid concentrations and marine mammal
and seabird poisoning events resulting from food web contamination.
66

Figure 1.6: Pseudo-nitzschia abundances (A) and particulate domoic acid concentrations (B)
from the Southern California Bight plotted on temperature-salinity diagrams. 649 values were
plotted for each parameter. Lowest toxin concentrations were plotted first, followed by
successively higher concentrations, in order to allow higher values (which are rarer) to be visible.
Pseudo-nitzschia abundances were plotted in order of lowest associated domoic acid
concentrations to highest.

67

Figure 1.7: Two-dimensional depictions (water depth and distance from shore) of temperature
(A), salinity (B), chlorophyll fluorescence (C) and colored dissolved organic matter (CDOM)
along an onshore-offshore transect off Newport Beach, Orange County, April 3, 2009 obtained
using an autonomous underwater vehicle (Slocum Glider). A surface manifestation of a
phytoplankton bloom is apparent as elevated chlorophyll fluorescence near the shore (left side of
panel C), which extends offshore as a subsurface chlorophyll maximum. The maximum in
CDOM in panel (D) is due to effluent discharge of a nearby sewage treatment plant. A MODIS
image obtained on the same day (E) indicates the nearshore surface-associated phytoplankton
bloom (as indicated by elevated chlorophyll fluorescence. The black line in (E) indicates the
onshore-offshore track of the glider for parameters depicted in A-D.
68

Figure 1.8: Yearly river discharge of three major rivers (summed discharge of Los Angeles,
Santa Ana, San Gabriel Rivers) along the coast in the southern counties of the Southern
California Bight (grey bars) for the years 2003-2015 plotted with yearly maximal detectable
particulate domoic acid (purple bars), and annual average particulate domoic acid (black bars).
Median river discharge for the entire period is shown as a black dashed line.

69

Figure 1.9: Daily temperature anomaly and absolute temperature time-series for the period
2005-2017. Time-series data were collected from shore station sensors located at Newport
Beach Pier, City of Newport Beach, Orange County (representative of the southern region of the
SCB: two panels on left), and Stearns Wharf, Santa Barbara, Santa Barbara County
(representative of the northern region of the SCB: two panels on right). Temperature anomalies
were calculated for each site from the time-series of temperature data collected from the shore
station sensors. Particulate domoic acid concentrations from the entire southern and northern
regions of the SCB (A,C, respectively) were temporally matched to positive or negative
temperature anomalies, and are plotted accordingly on the figures (black dots). The righthand
axes are duplicated above and below a temperature anomaly of zero to denote whether
concentration data were matched to a positive or negative temperature anomaly. Only particulate
domoic acid concentrations ≥1 µg L
-1
(representing substantive concentrations of particulate
domoic acid) were plotted. Absolute surface water temperatures for the same period are shown
for Newport Beach Pier (B) and Stearns Wharf (D). Note the differences in maximal absolute
temperatures between the two sites.
70

Figure 1.10: Two examples of toxic Pseudo-nitzschia blooms on the San Pedro Shelf during
2006 and 2007, showing concentrations of particulate domoic acid, and their correspondence to
domoic acid concentrations in the fluids and excreta of animals stranding during the same period.
(A,B) Maximal concentrations and geographical distribution in the study area during blooms in
the two years (black dots indicate sampling stations; particulate domoic acid concentrations in µg
L
-1
). LA hb indicates the Los Angeles harbor; black lines show the breakwater. Data for marine
mammals are shown in (C) and seabirds are shown in (D). Concentrations of particulate domoic
acid (green lines: values are averages for 20 samples per cruise), total numbers of stranded or
dead animals (black lines with gray fill) and animals testing positive for domoic acid (red lines
with stippled fill) along the SCB during 2006 and 2007.

# of pinnipeds
Particulate Domoic Acid (µg L
-1
)

# of birds
1/1/06
2/26/06
4/23/06
6/18/06
8/13/06
10/8/06
12/3/06
1/28/07
3/25/07
5/20/07
7/15/07
9/9/07
16
14
12
10
8
6
4
2
0
16
14
12
10
8
6
4
2
0
80
70
60
50
40
30
20
10
0
80
70
60
50
40
30
20
10
0
PV
LB
HB
A
NB

10 km
LA hb
B
C
D
March 17, 2006 April 27, 2007

25.01-30
20.01-25
15.01-20
10.01-15
7.51-10
5.01-7.5
2.51-5.0
1.01-2.5
>0.01-1
pDA (µg L
-1
)

Particulate Domoic Acid
DA-positive individuals
Total individuals stranded
71
Table 1.1: Fifteen year medians (2003-2017) in domoic acid concentrations (µg L
-1
) and
chlorophyll a concentrations (µg L
-1
) in relation to the positive or negative phase of the Pacific
Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO) and Multivariate ENSO
Index (MEI). Analyses were conducted using the median of particulate domoic acid
concentrations were above the limit of detection and the corresponding chl a concentrations
(where available). Bolded values are significantly different at p ≤ 0.05; n = number of
measurements included in the comparisons.

Variable
Time
Lag
Median pDA
concentration
n p
Median Chl a
concentration
n p
PDO (+)
None
0.177 841
0.845
4.26 606
<0.001
PDO (-) 0.183 807 2.83 630
PDO (+)
1
month
0.167 939
0.569
3.91 692
<0.001
PDO (-) 0.170 767 2.84 599
NPGO (+)
None
0.136 1216
<0.001
2.78 853
<0.001
NPGO (-) 0.264 457 4.75 438
NPGO (+)
1
month
0.130 1188
<0.001
2.76 825
<0.001
NPGO (-) 0.277 492 4.65 466
MEI (+)
None
0.130 959
<0.001
2.74 633
<0.001
MEI (-) 0.260 747 3.99 658
MEI (+)
1
month
0.114 826
<0.001
2.73 602
<0.001
MEI (-) 0.262 880 3.82 689
72
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and toxicity of Pseudo-nitzschia while using different nitrogen sources. Harmful Algae 8,
792-810.
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R.M., Ruttenberg, K., Sevadjian, J., 2014. Hidden thin layers of toxic diatoms in a coastal
bay. Deep-Sea Res. II 101, 129-140.
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109-121.
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Woodruff, D., 2000. Domoic acid production near California coastal upwelling zones, June
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Trainer, V.L., Cochlan, W.P., Erickson, A., Bill, B.D., Cox, F.H., Borchert, J.A., Lefebvre, K.A.,
2007. Recent domoic acid closures of shellfish harvest areas in Washington State inland
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A., Moore, S.K., 2009. Variability of Pseudo-nitzschia and domoic acid in the Juan de Fuca
eddy region and its adjacent shelves. Limnol. Oceanogr. 54, 289-308.
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Trainer, V.L., Suddleson, M., 2005. Monitoring approaches for early warning of domoic acid
events in Washington State. Oceanography 18, 228-237.
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acuminata during an upwelling-downwelling cycle in a Galician Ría. Limnol. Oceanogr. 53,
1816-1834.
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86
Chapter Two: Pseudo-nitzschia species composition varies concurrently with
domoic acid concentrations during two different bloom events in the Southern
California Bight

Jayme Smith,
1
Alyssa G. Gellene,
1
Katherine A. Hubbard,
2
Holly A. Bowers,
3,4
Raphael M.
Kudela,
5
Kendra Hayashi,
5
David A. Caron
1

1
Department of Biological Sciences, University of Southern California, Los Angeles, California
90089, USA
2
Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission,
Saint Petersburg, Florida 33701, USA
3
Monterey Bay Aquarium Research Institute, Moss Landing, California 95039, USA
4
Moss Landing Marine Laboratories, Moss Landing, California 95039, USA
5
Ocean Sciences Department, University of California, Santa Cruz, California 95064, USA

This work is published in the Journal of Plankton Research, Volume 40, Issue 1, 1 January 2018,
Pages 29–45, https://doi.org/10.1093/plankt/fbx069
87
Chapter Two Abstract
The San Pedro Shelf (SPS) region of the Southern California Bight has witnessed an increase of
Pseudo-nitzschia spp. blooms during the past decade, although the domoic acid (DA)
concentrations observed during these events have varied considerably. This study compared the
extent, timing, and environmental controls of Pseudo-nitzschia blooms that were observed in two
consecutive years on the SPS. Environmental conditions were characterized during shipboard
surveys during spring 2013 and 2014 along an onshore-offshore transect at surface and
subsurface depths. A Pseudo-nitzschia bloom of similar cell abundances was observed during
each year, yet maximal domoic acid concentrations differed by nearly two orders of magnitude.
Environmental parameters were favorable for Pseudo-nitzschia spp. growth in both years, but
few factors could be identified that specifically pertained to DA, with the exception that toxicity
correlated negatively with dissolved silicic acid concentrations. Automated ribosomal intergenic
spacer analysis (ARISA) for Pseudo-nitzschia species indicated that the relative abundance of
toxin-producing species had a strong influence on DA concentrations between years, with high
domoic acid concentrations corresponding to Pseudo-nitzschia communities dominated by P.
australis/P. seriata. Factors explaining the preferential growth of particular Pseudo-nitzschia
species remain enigmatic but are important for predicting toxic events attributable to these taxa.
88
2.1 Introduction
Pseudo-nitzschia is a cosmopolitan genus of pennate diatom that is known to produce
the water-soluble neurotoxin domoic acid (DA) (Bates et al., 1989). The genus is commonly
observed in upwelling systems, where it can form toxic blooms (Trainer et al., 2012). DA
accumulates in the food web via trophic transfer, contaminating fisheries and presenting human
health hazards. Consumption of contaminated seafood can cause Amnesic Shellfish Poisoning
(ASP) in humans, which manifests clinically with symptoms of diarrhea, gastrointestinal pain,
disorientation and memory loss, and in extreme cases, can cause death. Closures of DA
contaminated fisheries can result in significant economic impacts (Wessells et al., 1995). Toxic
Pseudo-nitzschia blooms can also have a negative impact on animal populations in marine
ecosystems, causing sickness and mass mortality events in marine animals, including sea lions,
sea otters, whales and seabirds (Fritz et al., 1992; Kvitek et al., 2008; Lefebvre et al., 2002;
Scholin et al., 2000).
There are more than 40 described species of Pseudo-nitzschia, although not all species
are toxigenic. At least 14 species of Pseudo-nitzschia have exhibited the ability to produce DA
under a variety of conditions (Lelong et al., 2012; Trainer et al., 2012), but domoic acid
production is not constitutive. Multiple environmental stressors appear to stimulate toxin
production, and DA production amongst and within toxigenic species can range several orders of
magnitude (Lelong et al., 2012; Thessen et al., 2009; Trainer et al., 2012). Therefore, it has been
difficult to establish a simple relationship between Pseudo-nitzschia cell abundance and bloom
toxicity in nature (Seubert et al., 2013).
The availability and form of macro- and micronutrients influence DA production in many
toxigenic Pseudo-nitzschia species. Culture and field studies have indicated that macronutrient
89
limitation (specifically phosphorus and silicate limitation) can trigger or enhance DA production
(Fehling et al., 2004; Pan et al., 1996a; Pan et al., 1996b). The form of nitrogen can also
modulate both toxin production and cell growth and this effect can vary by species and strain
(Cochlan et al., 2008; Howard et al., 2007; Kudela et al., 2008; Thessen et al., 2009; Thessen et
al., 2005). Iron limitation, copper toxicity (Maldonado et al., 2002; Rue and Bruland, 2001;
Wells et al., 2005) and increased partial CO
2
concentrations (Tatters et al., 2012) have also been
reported to increase DA production. Additionally, studies have shown that combined
environmental stressors such as silicate limitation and increased partial CO
2
concentrations can
synergistically enhance cell toxicity (Tatters et al., 2012). Physical factors, such as upwelling
(Kudela et al., 2005; Schnetzer et al., 2013), mesoscale eddies (Anderson et al., 2006), and water
column structure (Ryan et al., 2014) have also been correlated with toxic bloom formation.
Overall, the environmental cues controlling bloom formation and toxin production appear to be
quite complex, involving a suite of chemical, physical and biological factors.
The presence of Pseudo-nitzschia species has been documented in the Southern
California Bight since the 1930’s (Lange et al., 1994), however, only recently has there been
documentation of DA and other HAB events in the region (Caron et al., 2010; Schnetzer et al.,
2013; Schnetzer et al., 2007). The San Pedro Shelf (SPS) is an emerging DA "hot spot" based on
the increasing frequency and concentrations of observed toxic events (Schnetzer et al., 2013).
Some of the highest recorded particulate DA concentrations (52.3 µg L
-1
) were measured in the
San Pedro Channel in 2011 (Stauffer et al., 2012). The coastal areas surrounding the SPS are
densely populated and are impacted by urban discharge. Anthropogenic nitrogen inputs in the
SPS region, in particular, ammonium from wastewater discharge, can rival the input of new
nitrogen from upwelling (Howard et al., 2014). This may play a role in regional bloom formation
90
(Nezlin et al., 2012) and perhaps the upsurge in DA events due to the influence of nitrogen form
on both toxin production and cell growth (Howard et al., 2007). The region is also characterized
by seasonal upwelling-favorable wind patterns (Nezlin and Li, 2003) and highly episodic rain
and river discharge events (Schnetzer et al., 2013; Seubert et al., 2013). Upwelling has been
linked to the formation of blooms in the region, resulting in toxic Pseudo-nitzschia blooms that
show strong seasonality, typically occurring during spring (Schnetzer et al., 2013; Seubert et al.,
2013). Nevertheless, while these general triggers for bloom initiation are known, the specific
factors leading to toxic events are not well constrained.
The goal of this study was to examine the extent, duration, and toxicity of Pseudo-
nitzschia blooms during spring on the SPS in the waters proximal to Newport Beach, California.
The physiochemical and biological variables associated with the Pseudo-nitzschia blooms were
measured at surface and subsurface chlorophyll maximum depths on a near-weekly basis during
the springtime period of March – April in 2013 and April – May in 2014. The relationships
between Pseudo-nitzschia composition, abundances, and toxicity were examined in relation to
environmental factors.
2.2 Materials and Methods
2.2.1 Study Site Description
Eleven shipboard surveys were conducted during springtime in 2013 and 2014 on the
SPS near Newport Beach, California to compare the biological and physiochemical parameters
that influence the extent, duration, and DA concentrations of Pseudo-nitzschia blooms. The
study consisted of four sampling stations along an onshore-offshore transect (Figure 2.1).
Shipboard surveys were conducted in 2013 on 8 March, 15 March, 17 March and 5 April on the
R/V Yellowfin and on 22 March on the R/V Rachel Carson. Surveys in 2014 were conducted on 2
91
April, 7 April, 25 April and 5 May on the R/V Yellowfin and 14 April and 18 April on the R/V
Rachel Carson.
2.2.2 Discrete Sample Collection
Discrete water samples were collected from the surface (~1-2 m depth) and the
subsurface chlorophyll maximum (SCM) (depths of the latter varied, Table 2.1) using a SBE32
water-sampling carousel. A total of 80 discrete samples, 36 in 2013 and 44 in 2014, were
collected and processed for a variety of analyses during the study. Vertical profiles of
environmental parameters were conducted at each station to locate the SCM, which was defined
as the highest chlorophyll fluorescence within the water column. Profiles of in situ parameters
were obtained using a Sea-Bird SBE911plus CTD equipped with a Wet Labs ECO-
FLNTU(RT)D fluormeter, SBE3plus temperature sensor, and SBE4C conductivity sensor (Sea-
Bird Electronics, Bellevue, WA) (Supplementary Figure 2.1). Salinity data were not available for
7 April, 25 April, and 5 May from the 2014 study due to instrument failure.
2.2.3 Chlorophyll a and Microalgal Community Composition
Total chlorophyll a (chl a) was analyzed fluorometrically via the non-acidification
method using a Trilogy Turner Designs fluorometer (Turner Designs, Sunnyvale, CA). Samples
for chl a analysis were collected by filtration onto glass fiber filters (Sterlitech, grade F, Kent,
WA). Sample volumes varied depending on the in situ chlorophyll fluorescence and ranged from
50-100 mL. Filters were extracted in 100% acetone at -20 °C in the dark for 24 hours.
Seawater samples were collected and preserved with 1% formalin (final concentration)
for determination of phytoplankton community composition (cells >10 µm in size). Preserved
samples were stored at 4 °C until analysis. Phytoplankton were enumerated using a Leica DM
IRBE inverted light microscope (Leica Microsystems, Buffalo Grove, IL) at 400x after settling
92
25 mL of the sample in Utermöhl chambers for approximately 24 hours (Utermöhl, 1958). The
counting method as applied yielded a limit of detection of ~3.0 x 10
3
cells L
-1
. Cells were
categorized according to four groupings: diatoms, dinoflagellates, grazers, and other. Organisms
within each group were identified to genus whenever possible.
2.2.4 Measurements of Particulate and Cellular Domoic Acid
Subsamples were collected in duplicate from all discrete water samples for particulate
domoic acid (pDA) analysis via gentle vacuum filtration of 200 mL of sample water onto glass
fiber filters. Filters were stored at -20 °C in the dark until analyzed. Filters were extracted in 3
mL of 10% methanol, sonicated for 15s and centrifuged for 15 min at 4,000 rpm. The
supernatant was analyzed via Mercury Science, Inc., DA Enzyme-Linked ImmunoSorbant Assay
(ELISA: Mercury Science, Durham, NC) according to the methods described in Litaker et al.
(2008). The method had a detection limit of 2.0 x 10
-2
µg L
-1
as applied in our samples. Samples
below detection were assumed to be zero for all calculations and statistical analyses. Cellular
domoic acid concentrations (cDA) were calculated from pDA concentrations and abundances of
Pseudo-nitzschia cells whenever there were detectable quantities of each parameter.
2.2.5 Analysis of Pseudo-nitzschia Species Composition using Automated Ribosomal Intergenic
Spacer Analysis (ARISA)
Six filters archived for pDA were opportunistically analyzed for Pseudo-nitzschia species
community composition via ARISA. ARISA is a community analysis technique that utilizes the
ITS1 region as a molecular marker to resolve Pseudo-nitzschia taxonomy in natural samples
(Hubbard et al., 2008). Samples were collected at Stations 2 and 4 on 15 March 2013, Stations 1
and 4 on 5 April 2013, Station 3 on 7 April 2014 and Station 3 on 5 May 2014. All samples were
collected at the surface. Samples were collected via gentle vacuum filtration of 200 mL of
93
sample water onto glass fiber filters and archived at -20 °C in the dark until extraction.
Environmental DNA was extracted using the DNeasy Plant Mini Kit (Qiagen Inc., Valencia, CA,
USA) and amplified in preparation for ARISA using the Pseudo-nitzschia-specific ITS1 primer
set PnAll F/R according to the methods outlined in Hubbard et al. (2014). Purification of PCR
products for ARISA was conducted using MultiScreen PCRµ96 filter plates (EDM Millipore,
Darmstadt, Germany), and 1 ng of product was analyzed on an ABI 3730 XL using a LIZ600
size standard. Electropherogram analysis with DAx software (Van Mierlo Software Consultancy,
Eindhoven, Netherlands) used the same peak calling criteria outlined in Hubbard et al. (2014).
To identify the novel 140 bp ARISA peak, direct sequencing was conducted on PCR product
resulting from successive PCRs with the PnAll primer pair. Products were visualized on a 3%
agarose gel following electrophoresis. The target band was excised and purified using the
QIAgen Gel Purification Kit (Qiagen Inc., Valencia, CA, USA), and sequenced bidirectionally
with the PnAll primers by Eurofins Genomics. Complementary sequences were aligned and
edited using Sequencher® version 5.1 DNA sequence analysis software (Gene Codes
Corporation, Ann Arbor, MI, USA) and identified by querying the Genbank nucleotide (nr/nt)
database with the BLAST function (Altschul et al., 1990); a consensus sequence was submitted
to GenBank (accession # MG195950). Other sequence data from U.S. west coast Pseudo-
nitzschia species (Carlson et al., 2016; Hubbard et al., 2014; Hubbard et al., 2008; Marchetti et
al., 2008; Smith et al., 2012) were utilized for ARISA peak assignments (Supplementary Table
2.1).
2.2.5 Dissolved Nutrient Measurements
Nitrate + nitrite (hereafter referred to as NO
x
-
), phosphate, silicic acid, ammonium, and
urea samples were collected via filtration through 0.22 µm acetate luer-lock syringe filters from
94
all discrete samples. Samples collected for analysis of NO
x
-
, phosphate and silicic acid were
collected in acid-rinsed plastic scintillation vials. Samples for urea and ammonium analysis were
collected in separate containers following the same procedure. Samples were frozen at -20 °C
until analyzed. NO
x
-
, phosphate and silicic acid were measured on a QuickChem 8500 Flow
Injection Analysis system (Lachat Instruments; Hatch Company, Loveland, CO). Urea was
measured spectrophotometrically according to the method described in Mulvenna and Savidge
(1992) on a Varian Cary 50 Bio UV/Visible Spectrophotometer (Varian Medical Systems, Palo
Alto, CA). Ammonium was measured fluorometrically on a TD-700 fluorometer (Turner
Designs, Sunnyvale, CA) according to the methods described in Holmes et al. (1999).
Atom:atom nutrient ratios were calculated for NO
x
-
:phosphate (N:P), NO
x
-
:silicic acid (N:Si),
silicic acid:NO
x
-
(Si:N) and silicic acid:phosphate (Si:P).
2.2.6 Regional Upwelling Estimates
Regional upwelling was determined using Bakun Upwelling Index (UI) values from a
NOAA/National Marine Fisheries Service (NMFS)/Pacific Fisheries Environmental Group
(PFEG) buoy located at 33ºN 119ºW (Figure 2.1), which is approximately 100 km offshore to
the southwest of the study region (the station closest to the study area). The UI provides an
estimate of upwelling strength based on the calculated upwelling-favorable wind forcing on the
ocean surface. Indices are expressed in units of cubic meters per second along each 100 meters
of coastline. Data were obtained from the PFEG online database (http://www.pfeg.noaa.gov/).
2.2.7 Instrumented Measurements using Wirewalker Vertical Profilers
Wirewalker vertical profilers (Pinkel et al., 2011; Rainville and Pinkel, 2001) were
moored near Station 2 (33.604°N, -118.023°W) and Station 3 (33.582°N, -118.038°W) during
the study (Figure 2.1). A wirewalker is a profiling platform that uses wave energy to gain
95
downward motion of a positively buoyant instrument array along a moored wire. The data
provided continuous vertical profiles of pertinent environmental variables as defined by attached
sensor packages. Wirewalkers were deployed during both years of this study but data are only
presented from the 2014 deployment. The 2013 deployment period ended prior to the bloom
event in April (data not presented here). Each wirewalker was outfitted with a WETLabs water
quality monitor (WQM) that was equipped with a Sea-Bird CTD (SBE-37) that yielded depth,
temperature and conductivity, a SBE-43 dissolved oxygen sensor, and a WETLabs optical
chlorophyll a and turbidity sensor. The WQM sampled at a rate of 1 Hz, yielding a vertical
resolution of ≈1 m following data analysis. The WQM had a 40-minute sampling regime during
each 1-hour period. The sensor package at Station 2 collected profiles from 2 – 25 m. Data were
collected from 31 March until 5 April (2014), after which time the instrument package
malfunctioned. Data collection resumed on 16 April after replacement of the instrument
package, and data collection continued until 5 May. A total of 871 profiles were collected prior
to sensor malfunction and 3965 profiles were collected after the sensor package malfunction was
corrected. The sensor package at Station 3 collected profiles from 2 – 47 m. A total of 2545
profiles were collected at Station 3 from 31 March until 16 April, after which time the data
collection failed.
2.2.8 Statistical Analysis
Temperature, salinity, dissolved nutrients, nutrient ratios, chlorophyll a, Pseudo-nitzschia
cell abundances and particulate domoic acid concentrations were organized into data sets that
were categorized by all data, data by year, and data by year and depth. Pairwise comparisons
were made between parameters in these data sets to test for bulk differences between years (e.g.
2013 vs. 2014) and year by depth (e.g. 2013 surface vs. 2014 surface). The Mann-Whitney rank
96
sum test was used to compare parameters and statistically significant differences were
determined at p<0.05 (Mann and Whitney, 1947). Spearman rank order correlation analysis was
used to determine the strength of association between pDA, cDA and Pseudo-nitzschia
abundances and environmental variables. The analysis was performed on the entire data set
(2013 + 2014) and by each individual year. A significant correlation was defined as variables
with a positive or negative Spearman’s correlation coefficient (ρ) with a p<0.05. Statistical
analyses were performed in SigmaPlot (v.11.0.0, Systat Software, Inc.).
2.3 Results
2.3.1 Chlorophyll a Concentrations and Phytoplankton Community Composition
Chlorophyll a was measured in a total of 80 discrete samples during the study. In 2013, a
total of seven samples exceeded chl a concentrations of a minor bloom (≥10 µg L
-1
; as defined
by Seubert et al. (2013) for the region)

and three additional samples exceeded major bloom
values (≥13 µg L
-1
). In 2014, five samples exceeded minor bloom concentrations and an
additional eleven samples exceeded concentrations of a major bloom (Figure 2.2A). Overall, chl
a concentrations between the two years overlapped, and there were no significant differences in
these values when averaged for each of the two years for samples collected at the surface or
SCM (p>0.05). The average chl a concentration in surface samples during 2013 was 5.19±3.46
µg L
-1
, while the average during 2014 was 6.04±7.67 µg L
-1
. Chl a in the SCM averaged
7.23±4.87 µg L
-1
and 8.30±8.40 µg L
-1
during 2013 and 2014, respectively.
Phytoplankton biomass was maximal each year during early April (Figure 2.2A). The
highest chl a concentrations in 2013 were observed on 5 April when several surface and SCM
samples exceeded major bloom concentrations (Figure 2.2A). Chl a in March 2013, prior to the
2013 bloom, was generally <5 µg L
-1
during each survey cruise, with a few peaks of >5 µg L
-1
at
97
offshore stations on 15 and 17 March. During the 2014 major bloom, concentrations of chl a on
2 April and 7 April, generally exceeded the maximal concentration measured in 2013, except for
a few stations (Figure 2.2A). Chl a in 2014 was generally low following the 7 April survey; chl a
was ≤2.5 µg L
-1
at the surface and ≤5.5 µg L
-1
at the SCM for the remaining survey cruises, with
the exception of one high value at the SCM on 18 April.
The vertically-profiling instruments deployed at Stations 2 and 3 (Figure 2.1) in 2014
documented the development of a phytoplankton bloom that co-occurred with near-surface
temperatures of 13-14 °C at both stations (Figure 2.3 A,B,D,E). Water temperature in the upper
10 m of the water column at Station 2 was 13-14 °C at the time of deployment on 31 March
(Figure 2.3A). An increase in chl a concentrations from ≤2 µg L
-1
to ≥10 µg L
-1
occurred between
1 and 5 April in the upper 10 m of the water column (Figure 2.3B). By 16 April, surface waters
were relatively warm (≈15-16 °C) until 27 April - 2 May (end of deployment), when
temperatures were 13-14 °C. Chl a was consistently ≤5 µg L
-1
after 16 April at Station 2. Water
temperatures of 13-14 °C were observed at Station 3 at a depth of 2-15 m at the time of
deployment on 31 March (Figure 2.3D). Chl a concentrations in that depth range increased from
≤2 to ≥6 µg L
-1
between 1 April and 9 April (Figure 2.3E) and attained a maximum of ≥10 µg L
-1
within the depth range 5-15 m. Concurrently with the presence of elevated phytoplankton
biomass, dissolved oxygen ranged from 10 to 15 mL L
-1
within the chl a feature, compared to a
background concentration of ≤5 mL L
-1
(Figure 2.3F). Chl a profiles after 7 April indicated pre-
bloom concentrations of ≤2 µg L
-1
. The decrease in chl a biomass occurred at the same time that
surface waters were 15-16 °C.
Absolute abundances of Pseudo-nitzschia cells overlapped between years, with similar
maximal abundances observed each year (Figure 2.2B). A major Pseudo-nitzschia bloom was
98
observed each year based on the definition of Seubert et al. (2013) of cell abundances ≥8.80 x
10
4
cells L
-1
.

Bloom abundances of Pseudo-nitzschia were observed on the 5 April survey at all
stations in 2013, and at all stations during the surveys conducted on 2 April and 7 April in 2014
(Figure 2.2B). Maximal cell abundances of 8.45 x 10
5
cells L
-1
were observed in 2013 and 8.58 x
10
5
cells L
-1
in 2014, on 5 April 2013 and 7 April 2014, respectively. Surveys conducted prior to
5 April 2013 and following 7 April 2014 showed Pseudo-nitzschia abundances that were
generally much lower than the abundances observed during the bloom events. Overall,
abundances of Pseudo-nitzschia ranged from BD (below detection) to 7.05 x 10
5
cells L
-1
at the
surface and from BD to 8.45 x 10
5
cells L
-1
at the SCM in 2013. Cell abundances ranged from
0.03 to 8.11 x 10
5
cells L
-1
in surface waters, and from BD to 8.58 x 10
5
cells L
-1
at the SCM
during 2014.
Diatoms were the dominant group within the microplankton community during the study
periods in both years. However, the relative contribution of Pseudo-nitzschia cells to the total
microplankton community varied between years. Common members of the phytoplankton
community during the 2013 study included Chaetoceros, Skeletonema, Thalassiosira,
Thalassionema, Navicula, and Cylindrotheca, and those genera typically outnumbered Pseudo-
nitzschia cells during the surveys conducted in March. However, Pseudo-nitzschia was the
numerically dominant member of the phytoplankton community (≥50% of microplankton)
during the 5 April survey at both depths (data not shown). In 2014, Pseudo-nitzschia was never
the dominant species in the microplankton community (~14-36% of the community). Other
chain-forming diatoms including Chaetoceros, Thalassiosira, and Leptocylindrus generally
outnumbered Pseudo-nitzschia (data not shown). Given the small cell volume of Pseudo-
nitzschia cells (~1000 µm
3
), they never dominated the biovolume of the phytoplankton
99
community.
2.3.2 Domoic Acid in the Plankton During 2013 and 2014
Particulate domoic acid (pDA) concentrations showed considerable differences in
maximal values and ranges between years. The maximal pDA concentration detected during the
5 April 2013 bloom was two orders of magnitude higher than the pDA concentration detected
during the bloom in 2014 (Figure 2.4) despite similar maximal Pseudo-nitzschia cell abundances
during both blooms. The maximal pDA concentration observed in 2013 was 17.4 µg L
-1
(5 April
survey, SCM), while the maximal value in 2014 was only 0.19 µg L
-1
(7 April survey, SCM).
The average pDA concentration of all samples collected in 2013 was 2.61±5.21 µg L
-1
, while the
average value in 2014 was 0.05±0.06 µg L
-1
. A >50-fold difference between average pDA
concentrations was observed between years, however, the difference in these averages was
heavily influenced by maximal pDA concentrations observed during bloom periods. Toxin was
not detected in every sample collected (samples below the detection limit were entered as 0).
Domoic acid showed considerable temporal variability throughout the study periods in both
years, with 44% of the 36 samples from 2013 with detectable concentrations (4 out of 5 surveys)
and 46% of the 44 samples (4 of the 6 surveys) in 2014. pDA concentrations during 2013,
excluding non-detects, ranged nearly 3 orders of magnitude with concentrations ranging 0.02-
12.6 µg L
-1
in the surface, and 0.03-17.4 µg L
-1
in the SCM (Figure 2.5A). Detectable
concentrations of pDA in 2014 ranged 0.06-0.17 µg L
-1
in the surface, and 0.05-0.19 µg L
-1
in
the SCM (Figure 2.5A).
Pseudo-nitzschia cells had higher DA quotas, on average, in 2013 than in 2014 (Figure
2.5B). The average cellular domoic acid concentration (cDA) in all samples collected in 2013
was 5.71±10.6 pg DA cell
-1
while the average cDA of all samples collected in 2014 was
100
0.26±0.87 pg DA cell
-1
. The difference in yearly cDA averages was skewed by the large
difference in toxin quotas calculated for the 2013 bloom compared to the 2014 bloom (Figure
2.5B). Cellular toxin concentrations in samples with detectable cDA spanned two orders of
magnitude in 2013, similar to the trend observed in pDA. Cellular toxin concentrations in
samples collected near the surface ranged from 0.36 to 36 pg DA cell
-1
and from 0.62-21 pg DA
cell
-1
in the SCM. Cellular DA concentrations during 2014 were 0.08-0.55 pg DA cell
-1
at the
surface, while cDA concentrations in the SCM ranged from 0.08 to 5.7 pg DA cell
-1
(Figure
2.5B).
Particulate and cellular domoic acid concentrations showed similar temporal patterns
between years with maximal toxin concentrations during each year occurring in early April
(Figure 2.5) despite large differences in absolute values between years (Figure 2.4). No
consistent lateral or vertical spatial patterns in toxin were detected between years along the
transect or between depths (Figure 2.5). Samples from the SCM generally had higher
concentrations of toxin than those at the surface during 2013, but not in 2014 (Figure 2.5A). The
average pDA concentration was 2-3 orders of magnitude higher between the surveys conducted
in March (pDA ≤ 0.1 µg L
-1
) and the 5 April 2013 survey when pDA concentrations ranged from
8.20-17.4 µg L
-1
(Figure 2.5). Toxin concentrations were similar (and low) between depths in
2014. Low concentrations (0.06±0.01 µg L
-1
) of pDA were detected on 2 April and showed little
difference among stations or depths (Figure 2.5). pDA concentrations were higher, by
comparison to 2 April, on 7 April and particulate and cellular toxin concentrations did not vary
much by stations or depth (pDA: 0.15±0.02 µg L
-1
). Toxin was not detected in any of the
samples from the remaining surveys after 7 April with the exception of low concentrations
(<0.1µg L
-1
) measured at two stations on 18 April (Figure 2.5A,B).
101

2.3.3 Pseudo-nitzschia Species Composition Changes and Toxin Concentrations
Pseudo-nitzschia species composition was assessed using ARISA, a genus-specific, PCR-
based method that identifies taxa based on amplicon sizes and estimates their relative abundance
based on the proportion of ITS1 copies from different species in the starting template (Hubbard
et al., 2014). This approach demonstrated shifts in community composition during both years
and between years (Figure 2.6). Ten fragment sizes were observed (Supplementary Table 2.1),
representing seven species (P. sabit, P. galaxiae, P. pungens, P. multiseries, P. fryxelliana, P.
decipiens and P. cuspidata), two potential species complexes that cannot be discriminated based
on fragment size (P. australis/P. seriata and P. heimii/P. americana), and one fragment that
could not be attributed to Pseudo-nitzschia species found along the U.S. west coast (unknown
amplicon of 223/224 base-pairs). The 140 base-pair sequence (MG195950) was 98% similar to
P. galaxiae, the closest species match in GenBank (e.g., DQ336158), and was differentiated only
by two short deletions. Several taxa observed with ARISA have never before been reported in
the coastal waters of the SCB, including P. sabit, P. decipiens and P. fryxelliana, indicating the
importance of further taxonomic study in this area. Most species detected were potential toxin-
producers except P. heimii/P. americana, P. sabit, P. fryxelliana and P. decipiens
(Supplementary Table 2.1).
ARISA was performed on three bloom samples collected during this study, when cell
abundances exceeded 1.0 x 10
5
cells L
-1
. pDA concentrations differed considerably between
these samples along with marked differences in Pseudo-nitzschia species composition. Samples
collected from Stations 1 and 4 during the bloom on 5 April 2013 showed dominance of P.
australis/P. seriata (100% and 96% of the ARISA signal, respectively) (Figure 2.6).
102
Concurrently, high abundances (7.0 x 10
5
cells L
-1
and 3.1 x 10
5
cells L
-1
) and elevated pDA
concentrations (12.6 µg L
-1
and 8.20 µg L
-1
) were observed at Stations 1 and 4, respectively.
The 7 April 2014 sample had comparable cell abundances relative to samples collected on 5
April 2013, however P. australis/P. seriata was not detected. The ARISA profile collected at
Station 3 in 2014 indicated a mixed assemblage comprised mainly of P. cuspidata (38%) and P.
heimii/P. americana (31%) with smaller proportions (<15%) of P. pungens, P. sabit, P.
decipiens, and the 223/224 base pair amplicon (Figure 2.6). The pDA concentration
corresponding to this sample, 0.17 µg L
-1
, was the highest concentration observed in surface
samples collected in 2014.
Community composition was also assessed for three samples collected when cell
abundances were below the major bloom criterion, and pDA concentrations were either low or
below detection. ARISA performed on samples collected on 15 March 2013 showed an
assemblage comprised of P. fryxelliana, P. cuspidata, P. pungens, P. multiseries, P. sabit, P.
heimii /P. americana , and P. australis/P. seriata, but the proportions of these species detected in
the offshore and nearshore samples differed. The offshore ARISA profile (Station 4) showed
high proportions of P. heimii /P. americana (39%) and P. pungens (28%) while the more
nearshore Station (2) showed a high proportion of P. australis/P. seriata (42%). Concurrently,
low concentrations of pDA (0.05 µg L
-1
) were detected at Station 2, while pDA was not detected
at Station 4. The ARISA profile collected on 5 May 2014 indicated higher proportions of P.
galaxiae (49%) relative to P. australis/P. seriata (15%). No toxin was detected during the 5 May
survey and cell concentrations were <1.4 x 10
4
cells L
-1
.

103
2.3.4 Pseudo-nitzschia Cell Abundances, Domoic Acid and Upwelling Events
The upwelling index (UI) was used as a metric to compare wind-driven upwelling
between years, as a possible driver of Pseudo-nitzschia blooms and/or domoic acid events. The
overall magnitude of regional upwelling was similar between years despite large differences in
maximal pDA concentrations (Figure 2.7A, B). The UI averaged 102 ± 61 m
3
s
-1
100m
-1
over the
period of March 7 – April 6 in 2013, while the UI averaged 111±73 m
3
s
-1
100m
-1
during the
2014 period of April 1 – May 6.
Each year’s maximal Pseudo-nitzschia abundances occurred after a series of moderate UI peaks
(Figure 2.7A, B). The highest Pseudo-nitzschia abundances, domoic acid concentrations, and chl
a concentrations observed in 2013 were on 5 April. Multiple UI peaks occurred over the period
preceding that date. Similarly, a succession of UI pulses occurred during 2014 prior to the 7
April 2014 survey, when the yearly maximal Pseudo-nitzschia abundances and chl a
concentrations were observed (Figure 2.7B).
2.3.5 Relationships Between Environmental Conditions, Pseudo-nitzschia Abundance and
Domoic Acid
Spearman rank correlation was used to examine the associations between Pseudo-
nitzschia cell abundance, pDA, cDA, and measured biological and physiochemical parameters
for the entire data set (2013 + 2014), and for each individual year (2013 and 2014). The strongest
correlation (p<0.05) was the positive correlation between Pseudo-nitzschia cell abundance and
pDA concentration (Table 2.2). Positive correlations were also observed between Pseudo-
nitzschia abundance and chl a across the study (Table 2.2). Silicic acid negatively correlated with
Pseudo-nitzschia abundance, pDA and cDA in the entire data set. When examined by individual
year, silicic acid negatively correlated with pDA and cDA in 2013, and with Pseudo-nitzschia
104
abundance in 2014. cDA negatively correlated with the Si:P ratio across the entire data set, while
Pseudo-nitzschia abundance, pDA and cDA negatively correlated with the Si:P ratio in 2013, but
not in 2014. Pseudo-nitzschia abundances and pDA negatively correlated with PO
4
3-

concentrations in 2014, but not in 2013 or the combined years data set. Pseudo-nitzschia
abundances, pDA and cDA correlated negatively with temperature in the entire data set and in
2014, but not in 2013. No correlations were determined between Pseudo-nitzschia cell
abundance, pDA or cDA and N:P ratios, N:Si ratios, or NO
x
-

concentrations.
Environmental conditions were compared between survey periods in 2013 and 2014 and
revealed differences in silicic acid concentrations between high- and low-toxin years. Surface
silicic acid concentrations were significantly higher in 2014 than in 2013 (p<0.05; Table 2.1).
Specifically, average silicic acid concentrations were particularly low on 17 March and again on
5 April compared to other cruises in 2013 (Supplementary Fig 2). The Si:P ratio was also
significantly lower in 2013 than in 2014 at the surface (p<0.05), indicating that silicic acid
limitation was more prevalent in surface waters in 2013 when maximal pDA concentrations were
concurrently higher. No significant differences were detected between years at either depth for
concentrations of any of the measured nitrogen species, PO
4
3-
, or N:P and N:Si ratios, however,
variations in these parameters were observed within each year, particularly during bloom and
non-bloom periods (Table 2.1, Supplementary Fig 2).
2.4 Discussion
The present study investigated the extent, duration, and DA concentrations of two
Pseudo-nitzschia blooms during spring 2013 and 2014 on the San Pedro Shelf (SPS) along the
coast of the central Southern California Bight. Pseudo-nitzschia blooms have become an annual
occurrence on the SPS during the last decade (Lewitus et al., 2012), yet the magnitude of these
105
events has varied greatly and the environmental drivers leading to toxic blooms in the region are
still not well resolved. A major Pseudo-nitzschia bloom was observed in both years of this study
with maximal abundances each year of up to ~8 x 10
5
cells L
-1
(Figure 2.2B). Prior studies on the
SPS reported bloom abundances ranging from 10
4
cells L
-1
to 10
6
cells L
-1
(Schnetzer et al.,
2013; Schnetzer et al., 2007; Seegers et al., 2015; Seubert et al., 2013; Stauffer et al., 2012).
Particulate toxin concentrations in the plankton during the present study were within the range of
those previous findings. Nonetheless, maximal pDA concentrations during 2013 and 2014
differed by approximately two orders of magnitude (17.4 and 0.19 µg L
-1
, respectively).
Particulate DA concentrations as high as 52.3 µg L
-1
have been observed in the Southern
California Bight (Stauffer et al., 2012), although detectable pDA concentrations have more
commonly ranged between ~0.05 µg L
-1
and ~27 µg L
-1
(Schnetzer et al., 2013; Schnetzer et al.,
2007; Seubert et al., 2013).
Overall, our results indicated a strong correlation between Pseudo-nitzschia abundances
and pDA during each year, yet the absolute values of the relationship between Pseudo-nitzschia
abundances and maximal pDA concentrations varied greatly between years (Figure 2.4).
Moreover, the differences in maximal pDA concentrations between years appeared to be a
consequence of differences in Pseudo-nitzschia species assemblages, which were quite different
during the 2013 and 2014 blooms (Figure 2.6). Maximal pDA concentrations during the study
were observed during a bloom dominated by P. australis/P. seriata on 5 April 2013. The bloom
during 2014 was comprised of a mixed assemblage of multiple species with P. cuspidata and P.
heimii/P. americana detected in the highest proportions. Concurrently, pDA concentrations in
2014 were two orders of magnitude lower than values observed in 2013 (Figure 2.6).
Published reports on toxigenic Pseudo-nitzschia cells have indicated that DA production
106
can vary several orders of magnitude between different species, as well as among different
strains of the same species (Bates et al., 1998). Studies conducted under laboratory conditions
have reported DA quotas from P. australis and P. seriata ranging from 0.0027 pg DA cell
-1
to 37
pg DA cell
-1
. In contrast, P. cuspidata has a relatively low cellular DA quota ranging from 0.019
to 0.031 pg DA cell
-1
and P. heimii and P. americana are considered to be non-toxigenic species
(reviewed in Trainer et al., 2012). Based on this information, the P. australis/P. seriata bloom
during 2013 presumably possessed a greater potential for toxin production than a bloom of a
mixed assemblage of low-DA and non-DA producing species (Supplementary Table 2.1) of
similar magnitude, partially explaining the observations of the present study. Cellular DA quotas
during the 5 April 2013 bloom ranged from 14 to 36 pg DA cell
-1
, which are in the upper range
of quotas observed in laboratory studies of P. australis and P. seriata (reviewed by Trainer et al.,
2012).
High DA quotas in the P. australis/P. seriata population in the present study may also
indicate that the cells during the 2013 bloom event were nutrient limited. DA production by
toxigenic species is not constitutive, and published studies have demonstrated that a range of
environmental conditions can regulate the DA quotas of toxigenic Pseudo-nitzschia cells
(reviewed in Lelong et al., 2012). Silicic acid drawdown in surface waters that occurred during
the 5 April 2013 bloom likely led to silicic acid limitation of P. australis/P. seriata cells that
dominated the assemblage (Table 2.2, Supplementary Figure 2.2), implicating this factor in high
DA production. Maximal pDA concentrations on 5 April 2013 were observed in surface water
samples when silicic acid concentrations were below detection (Supplementary Figure 2.2).
Previous work has shown that silicic acid limitation is strongly linked to DA production, both in
the field and in culture (Anderson et al., 2006; Fehling et al., 2004; García-Mendoza et al., 2009;
107
Pan et al., 1996a).
Year-to-year variability in maximal pDA concentrations observed on the SPS, therefore,
appears to indicate a combined influence of species composition of the Pseudo-nitzschia
assemblage and the effect of physiochemical conditions on toxin production. Natural
communities are typically populated by a mix of different Pseudo-nitzschia species (Bates et al.,
1998), or a mix of different strains of the same species (Parsons et al., 1999), which may have
varying rates of DA production in response to physiochemical conditions. Studies conducted in
the SPS have attributed blooms with elevated pDA concentrations to several toxin-producing
species (Table 2.3). The majority of bloom events in the region with high pDA concentrations
(>10 µg L
-1
) have coincided with high abundances (>1.0 x 10
5
cells L
-1
) of P. australis, with the
exception of a bloom of P. cf. cuspidata in 2003 (Table 2.3; Schnetzer et al., 2007, Schnetzer et
al., 2013). Blooms with lower maximal pDA concentrations (<3 µg L
-1
) during 2004 and 2005
were dominated by P. australis and P. delicatissima, respectively (Schnetzer et al., 2013;
Schnetzer et al., 2007).
Year-to-year variability in maximal pDA concentrations has also been observed in other
bloom hot spots along the U.S. west coast, including the Juan de Fuca eddy region of the
Washington coast, Monterey Bay, CA, and the Santa Barbara Channel, CA (Anderson et al.,
2006; Anderson et al., 2009; Bargu et al., 2012; Lewitus et al., 2012; Trainer et al., 2000; Trainer
et al., 2009a; Trainer et al., 2009b; Walz et al., 1994). Blooms in those regions have shown wide
variations in particulate DA concentrations, similar to the SPS, ranging from below detection to
~25 µg L
-1
. Blooms in those regions have also been populated by a variety of toxigenic species
including P. australis, P. cf. pseudodelicatissima, P. cuspidata, P. multiseries, P. fraudulenta
and P. pungens. Particulate DA concentrations observed in those studies have been linked to a
108
range of different environmental conditions, including low dissolved nutrient concentrations,
imbalances in nutrient ratios, temperature anomalies, and circulation patterns. As observed in the
present study, high variances in pDA concentrations do indeed appear to be partly a consequence
of a ‘generalized’ Pseudo-nitzschia response to physiochemical parameters, but we speculate that
pDA concentration is also a consequence of how physiochemical conditions select for particular
species or strains within the overall Pseudo-nitzschia assemblage. Therefore, understanding the
interplay between environmental factors and their effects on Pseudo-nitzschia strain/species
composition (and attendant physiological capabilities) should substantively improve our ability
to predict future DA events.
The development of Pseudo-nitzschia blooms along the U.S. west coast has historically
been linked to upwelling (Lewitus et al., 2012; Trainer et al., 2000). Schnetzer et al. (2013)
correlated upwelled water to toxigenic P. australis blooms on the SPS (see their Figure 7), and
elevated Pseudo-nitzschia abundances have been shown to co-occur with upwelling conditions in
many bloom ‘hot spots’ along the west coast of the U.S. (Bates et al., 1998; Kudela et al., 2005;
Lange et al., 1994; Schnetzer et al., 2013; Trainer et al., 2000). Wilkerson et al. (2006) found that
short periods of upwelling-favorable wind conditions followed by periods of relaxation appear to
specifically favor diatom growth. This is likely due to the time that cells need to respond
physiologically to upwelled nutrients (Kudela et al., 1997), and manifests itself as time-lagged
growth response to upwelling events. Our study supports those observations showing that both
the timing and magnitude of upwelling events is an important factor in the accumulation Pseudo-
nitzschia cells. Specifically, in both years a series of upwelling and relaxation events favored
Pseudo-nitzschia bloom development, as revealed by the regional upwelling index (Figure 2.7).
Our results do not indicate, however, that upwelling controlled differences in toxin
109
concentrations observed between years.
Our work indicates that specific, though perhaps subtly distinct, physiochemical
parameters resulted in the differences in Pseudo-nitzschia species dynamics between years.
Previous studies examining Pseudo-nitzschia species dynamics have concluded that different
Pseudo-nitzschia species appear to be associated with specific environmental factors such as
temperature, nutrient concentrations, and nutrient ratios (Bates et al., 1998; Fernandes et al.,
2014; Guannel et al., 2015; Hubbard et al., 2014; Ruggiero et al., 2015). Upwelling clearly
stimulated Pseudo-nitzschia growth in the SPS region during our study, but the specific
conditions that promoted the dominance of P. australis/P. seriata in 2013 and a mixed Pseudo-
nitzschia assemblage in 2014, and therefore the overall toxin levels appearing during these
blooms, remain unclear. nMDS analysis was unable to resolve clear differences in
physiochemical conditions between the 5 April 2013 and 7 April 2014 cruises when the species
compositions were very different (Supplementary Figure 2.3). This result may indicate that the
physiochemical conditions leading to differences in Pseudo-nitzschia assemblages may have
been extremely subtle or potentially related to an unmeasured parameter such as iron or vitamins.
It also may indicate that the conditions measured at the time of peak toxin concentrations of the
blooms were not representative of the conditions that favored the development of the toxigenic
or non-toxigenic populations, as has previously been suggested by Schnetzer et al. (2007).
Alternatively, or additionally, upwelling could have advected an outside or deep water mass
containing a toxin-producing seed population into the local environment, which then developed
into a bloom in surface waters where conditions were more favorable for growth (Anderson et
al., 2006; Seegers et al., 2015; Trainer et al., 2000). Such time-lagged relationships or advective
influences are difficult to resolve from ship-based sampling programs.
110

2.5 Conclusions
Our study demonstrated that the species composition of the Pseudo-nitzschia assemblage
strongly affected the concentrations of DA that occurred during blooms in successive years in the
same location. Similar abundances of Pseudo-nitzschia were observed during both blooms, yet
maximal domoic acid concentrations differed by almost two orders of magnitude between 2013
and 2014. High domoic acid concentrations corresponded with a bloom overwhelmingly
dominated by P. australis/P. seriata, while a bloom with a mixed Pseudo-nitzschia assemblage
had very low domoic acid concentrations. In both years, the growth of Pseudo-nitzschia cells
seemed to be driven by periods of upwelling followed by relaxation. Low dissolved silicic acid
concentrations, which have been previously linked to enhanced domoic acid concentrations,
were correlated with high pDA concentrations and cellular DA quotas in 2013. Our study points
to the importance of understanding not only the factors that lead to Pseudo-nitzschia bloom
development but also the factors that control species dynamics within the Pseudo-nitzschia
assemblage. Given the importance of species composition on potential DA concentrations, it is
fundamentally important to understand the subtle chemical, physical and biological factors that
determine Pseudo-nitzschia composition. Without a better understanding of the factors driving
blooms of toxigenic Pseudo-nitzschia species, prediction of the timing and magnitude of
toxigenic blooms in the region will remain elusive.
111
2.6 Chapter Two Figures and Tables

Figure 2.1: Map of shipboard survey stations in the waters of the San Pedro Shelf near Newport
Beach, CA. The insert map depicts the California coastline with a dashed black box outlining the
study site within the Southern California Bight. Depth contours are shown on the legend. Water
depths at Stations 1-4 were 15, 30, 57 and 190 m, respectively. Wirewalker profiling instruments
were moored at Station 2 and Station 3. The location of the NOAA/NMFS/PFEG buoy used to
calculate the regional Bakun Upwelling Index data is indicated with a black x.
Los Angeles
San Francisco
112

Figure 2.2: (A) Chlorophyll a concentrations (µg L
-1
) versus sampling date for 2013 and 2014.
Solid line marks the criteria for a major phytoplankton bloom (≥13 µg chl a L
-1
) and the dotted
line shows the criteria for a minor bloom (≥10 µg L
-1
) according to Seubert et al. (2013). Closed
diamonds represent 2013 surface chl a concentrations, closed circles represent 2013 SCM chl a
concentrations, open squares represent surface 2014 chl a concentrations, and open triangles
represent SCM 2014 chl a concentrations. (B) Pseudo-nitzschia cell abundances (10
5
cells L
-1
)
from each sampling date in 2013 and 2014. Solid line marks the criteria for a major Pseudo-
nitzschia bloom (≥8.80 x 10
4
cells L
-1
) and the dotted line shows the criteria for a minor bloom
(≥4.00 x 10
4
cells L
-1
) according to Seubert et al. (2013). Closed diamonds represent 2013
surface Pseudo-nitzschia abundances, closed circles represent 2013 SCM Pseudo-nitzschia
abundances, open squares represent 2014 Pseudo-nitzschia abundances at the surface, and open
triangles represent SCM Pseudo-nitzschia abundances.
b
a
Cells (10
5
cells L
-1
) Chlorophyll a (µg L
-1
)
2013 Surface
2013 SCM
2014 Surface
2014 SCM
113

Figure 2.3: In situ data from the wirewalkers moored at Station 2 (nearshore) and Station 3
(offshore) in 2014. (A) Temperature (°C), (B) Chlorophyll a (µg L
-1
) and C) Dissolved oxygen
(mL L
-1
) at Station 2. (D) Temperature (°C), (E) Chlorophyll a (µg L
-1
) and (F) Dissolved
oxygen (mL L
-1
) at Station 3. Data gaps are shown as grey areas.
a
b
c
Temperature (°C) Chl a (µg L
-1
)
20
15
10
5
Oxygen (mL L
-1
)
20
15
10
5
20
15
10
5
20
15
10
5
31 Mar 16 Apr 5 May 26 Apr 5 Apr
d
f
Temperature (°C) Chl a (µg L
-1
) Oxygen (mL L
-1
)
9
8
7
6
5
4
3
5
15
25
35
45
5
15
25
35
45
5
15
25
35
45
31 Mar 16 Apr 5 May
e
Meters (m)
17
16
15
14
13

12
11
10
9
8
7
6
5
4
3

17
16
15
14
13
12
11
10

20
15
10
5

114

Figure 2.4: (A) Particulate domoic acid concentrations versus corresponding Pseudo-nitzschia
cell abundances for 2013 (data shown as open black diamonds) and for 2014 (data shown as
open black squares). The equation of the 2013 regression line is y = 2.1201x10
-5
x - 0.3938, R² =
0.9236, p< 0.0001. (B) Plot of 2014 pDA versus Pseudo-nitzschia cell abundances on an
expanded y-axis (data shown as open black squares). The equation of the 2014 regression line is
y = 1.6283x10
-7
x + 0.0047, R² = 0.6984, p< 0.0001.
115
Figure 2.5: (A) Heat map of particulate domoic acid concentrations ordered by station number
(columns) and date (rows) for samples collected at the surface and at the depth of the SCM,
showing the spatial and temporal trends in pDA concentrations. The color key (lower left)
indicates pDA concentrations in µg DA L
-1
ranging from ≤0.02 to 17.4. An open circle indicates
that the sample was below detection (BD) and no circle indicates the absence of a measurement.
(B) Heat map of cellular domoic acid concentrations ordered by station (columns) and date
(rows) for samples collected at the surface and at the depth of the SCM. The color key (lower
left) indicates cDA concentrations in pg DA cell
-1
ranging from 0 to 36.2 pg DA cell
-1
. An open
circle indicates that the sample was below detection and no circle the absence of a measurement.
≤0.02 17.4
pDA (µg L
-1
)
≤0.02
cDA (pg DA cell
-1
)
36
a b
2013
2014
116

Figure 2.6: Relative proportions of Pseudo-nitzschia species from ARISA (in relative
fluorescence units) for each of the six samples analyzed from 2013 and 2014. Each bar is labeled
with the date and station where the sample was collected. Unk. 223/224 refers to an ARISA
fragment of 223/224 base pairs that does not correspond to any known Pseudo-nitzschia species.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
15 Mar 13
Station 2
15 Mar 13
Station 4
5 Apr 13
Station 1
5 Apr 13
Station 4
7 Apr 14
Station 3
5 May 14
Station 3
Relative Fluorescence Units
P . galaxiae
Unk. 223/224
P . fryxelliana
P . decipiens
P . sabit
P . pungens
P . heimii/P . americana
P . multiseries
P . cuspidata
P . australis/P . seriata
117
Figure 2.7: Pseudo-nitzschia cell abundance, average pDA concentration and the calculated
Upwelling Index versus date for the study periods in (A) 2013 and (B) 2014. Panels (A) and (B)
are offset to allow for the overlap of dates that occur in both years. (A) The Upwelling Index
(Bakun Index Values from NOAA/NMFS/PFEG for: 33°N 119°W) for 2013 is shown with a
black line, Pseudo-nitzschia cell abundances are shown by diamonds and average pDA
concentrations for each survey date are shown by horizontal bars. (B) Upwelling Index for 2014
is shown with a black line, Pseudo-nitzschia cell abundances are shown with squares, and
average pDA concentrations for each survey date are shown by horizontal bars. The black
horizontal dashed lines in (A) and (B) indicate delineation between upwelling and downwelling
meteorological events in both panels.
b
pDA (µg L
-1
)
Pseudo-nitzschia (10
5
cells L
-1
)
Upwelling Index (m
3
s
-1
100 m
-1
)
pDA (µg L
-1
)
Pseudo-nitzschia (10
5
cells L
-1
)
Upwelling Index (m
3
s
-1
100 m
-1
)
2013 Pseudo-nitzschia
2013 Upwelling Index
2013 Average pDA (µg L
-1
)
a
2014 Pseudo-nitzschia
2014 Upwelling Index
2014 Average pDA (µg L
-1
)
Table 2.1: Dissolved nutrient concentrations and ratios for all stations at the surface and the subsurface chlorophyll maximum (SCM)
during spring 2013 and 2014 on the San Pedro Shelf. Dissolved nutrient concentrations: NO
x
-
(nitrate+nitrite), ammonium, urea,
phosphate, and silicic acid are reported in µM. Ratios of NO
x
-
:phosphate (N:P), NO
x
-
:silicic acid (N:Si) and silicic acid:phosphate
(Si:P) are reported as atom:atom. The mean, standard deviation (SD), range, number of samples (n), number of samples below the
detection limit (BD), and average depths with standard deviation are shown.

Mean SD Range n BD Mean SD Range n BD
Surface 2013 (1.8 ± 0.4 m) Surface 2014 (1.8 ± 0.4 m)
NO
x
-

0.97 1.60 0-6.09 18 4 1.18 1.46 0.05-4.96 21 0
NH
4
+

0.18 0.33 0-1.36 18 5 0.18 0.15 0.03-0.68 22 0
Urea
0.09 0.03 0.04-0.16 18 0 0.11 0.07 0.05-0.35 22 0
PO
4
3-

0.27 0.16 0-0.67 18 2 0.23 0.11 0.09-0.41 21 0
SiO
4
2-

1.65 2.08 0-7.62 18 3 3.12 2.47 0.75-7.08 21 0
N:P
4.68 10.7 0-36.3 18 6 3.81 3.71 0.32-13.5 21 0
N:Si
0.63 0.77 0-2.60 18 6 0.29 0.25 0.02-0.89 21 0
Si:P
4.84 5.81 0-17.0 18 5 12.1 5.98 4.65-23.5 21 0
SCM 2013 (12.9 ± 3.6 m) SCM 2014 (15.0 ± 1.2 m)
NO
x
-

2.55 2.50 0-8.11 18 1 4.28 4.65 0.21-14.6 21 0
NH
4
+

0.44 0.62 0-2.51 18 4 0.37 0.56 0.02-2.39 22 0
Urea
0.09 0.09 0-0.44 18 1 0.10 0.04 0.04-0.18 22 0
PO
4
3-

0.37 0.13 0.13-0.64 18 0 0.49 0.29 0.18-1.17 21 0
SiO
4
2-

2.93 1.61 0-5.78 18 1 5.81 4.14 1.18-14.3 21 0
N:P
8.30 9.14 0-26.5 18 1 6.86 4.82 0.76-15.3 21 0
N:Si
0.77 0.63 0-2.09 18 1 0.58 0.34 0.08-1.02 21 0
Si:P
8.92 6.09 0-25.2 18 1 11.2 3.53 4.82-16.6 21 0
119
Table 2.2: Spearman rank order correlation results between Pseudo-nitzschia abundance, pDA or cDA and measured physiochemical
parameters. These analyses were conducted using data sets of all data combined, and by individual year. PN = Pseudo-nitzschia
abundance. NO
x
-

= NO
3
-
+NO
2
-
. Temp = temperature. Sal = salinity. Bolded values are significant at p≤0.05; n = number of pair-wise
comparisons.

PN Chl a Temp Sal NO
x
-

NH
4
+
Urea PO
4
3-
SiO
4
2-
N:P N:Si Si:P n
2013 + 2014
PN 0.654 -0.243 -0.227 0.094 -0.198 -0.311 -0.210 -0.225 -0.071 -0.022 -0.218 56-80
pDA 0.775 0.663 -0.304 -0.150 -0.086 -0.314 -0.224 -0.179 -0.248 -0.063 0.043 -0.195 56-80
cDA 0.529 -0.287 -0.009 -0.109 -0.279 -0.130 -0.120 -0.285 -0.101 0.044 -0.255 56-80
2013
PN 0.653 -0.187 0.117 0.022 -0.235 -0.066 0.205 -0.145 -0.110 -0.041 -0.434 36
pDA 0.761 0.393 -0.023 0.017 -0.144 -0.485 -0.022 -0.073 -0.401 -0.172 -0.054 -0.474 36
cDA 0.319 0.056 0.002 -0.190 -0.456 0.045 -0.118 -0.472 -0.199 -0.037 -0.529 36
2014
PN 0.664 -0.368 -0.766 -0.226 -0.168 -0.536 -0.507 -0.396 -0.050 0.004 -0.130 20-44
pDA 0.803 0.818 -0.559 -0.444 -0.016 -0.010 -0.416 -0.321 -0.150 0.126 0.153 0.102 20-44
cDA 0.684 -0.541 -0.293 0.001 0.008 -0.260 -0.194 -0.068 0.087 0.107 0.095 20-44
120
Table 2.3: Summary of literature reports of Pseudo-nitzschia species, maximal DA concentrations and maximal cell abundances
associated with blooms occurring in the San Pedro Shelf region.
Year
Dominant Pseudo-nitzschia
species
Maximal pDA
Concentration (µg L
-1
)
Maximal Cell
abundances
(cells L
-1
)
Reference
2003 P. cf. cuspidata 12.7 Not Reported Schnetzer et al., 2007
2004 P. australis 1.94 5.3 x 10
4
Schnetzer et al., 2007
2005 P. delicatissima 2.91 Not Reported Schnetzer et al., 2013
2006 P. australis 14.4 1.44 x 10
6
Schnetzer et al., 2013
2007 P. australis 27.0 1.12 x 10
6
Schnetzer et al., 2013
2013 P. australis/P. seriata 17.4 8.45 x 10
5
This study
2014 P. cuspidata/P. hemii/P. americana 0.19 8.58 x 10
5
This study

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Domoic acid-producing diatom blooms in Monterey Bay, California: 1991-1993. Nat.
Toxins. 2(5), 271-279.
Wells, M.L., Trick, C.G., Cochlan, W.P., Hughes, M.P., Trainer, V.L., 2005. Domoic acid: the
synergy of iron, copper, and the toxicity of diatoms. Limnol. Oceanogr. 50(6), 1908-
1917.
Wessells, C.R., Miller, C.J., Brooks, P.M., 1995. Toxic Algae Contamination and Demand for
Shellfish: A Case Study of Demand for Mussels in Montreal. Mar. Resour. Econ. 10(2),
143-159.
Wilkerson, F.P., Lassiter, A.M., Dugdale, R.C., Marchi, A., Hogue, V.E., 2006. The
phytoplankton bloom response to wind events and upwelled nutrients during the CoOP
WEST study. Deep Sea Res. II 53(25-26), 3023-3048.
129
Chapter Three: Nearshore wastewater effluent discharge generates three
distinct phytoplankton blooms: A ‘natural’ experiment in Santa Monica Bay,
California

Jayme Smith
1
, Carter Ohlmann
2
, Mas Dojiri
3
, Curtis Cash
4
, Melissa Abderrahim
1
and David A.
Caron
1

1
Department of Biological Sciences, University of Southern California, Los Angeles, California,
90089, USA
2
Earth Research Institute, University of California, Santa Barbara, California, 93106, USA
3
LA Sanitation, 1149 S. Broadway St., Los Angeles, CA 90015
4
City of Los Angeles Environmental Monitoring Division, Playa del Rey, California, 90293,
USA
130
Chapter Three Abstract
The influence of anthropogenic nutrient loading from wastewater effluent discharge, relative to
natural nutrient sources such as upwelling, on phytoplankton dynamics within the Southern
California Bight has received considerable attention in recent years. Hyperion Treatment Plant
(HTP; Los Angeles, CA) discharges approximately 8.5 x 10
8
L day
-1
into Santa Monica Bay
(SMB). Secondarily-treated effluent is discharged from a pipe that is 8.1 km from shore, below
the euphotic zone (depth of 57 m) under normal operating conditions. Infrastructure repairs to
HTP’s principal outfall in autumn 2015 required the diversion of its chlorinated effluent to the
nearshore environment through an auxiliary 1.2 km outfall (depth of 18 m) for six weeks.
Shipboard surveys of SMB were conducted prior to, during, and after the diversion to track the
response of the plankton to the input of nutrients (~2.7 x 10
6
moles NH
3
-N day
-1
) from the
diverted effluent. Three temporally and taxonomically distinct phytoplankton blooms were
observed during the diversion with chlorophyll values that far exceeded regional thresholds for
major bloom events. Blooms dominated by diatoms, euglenoids, and raphidophytes were
observed in sequence, and each occurred in different regions of the bay. Blooms were most
intense in near-surface waters, within 1 - 2 km of shore. Potential harmful algal genera including
Pseudo-nitzschia spp., Chattonella marina, and Heterosigma akashiwo were observed during the
diversion, but algal toxins were not detected during the diversion. Small concentrations of
domoic acid were detected from samples collected following the return of effluent discharge to
the 8.1 km pipe.
131
3.1 Introduction
The Southern California Bight (SCB) is an eastern boundary current ecosystem that extends
700 km along of the North American West Coast from Point Conception, California, to Cabo
Colnett, Mexico. The region is characterized by seasonal upwelling events in the springtime that
stimulate much of the region’s primary production (Checkley and Barth, 2009; Hickey, 1992).
Upwelling has generally been considered the dominant source of nutrients to regions along
eastern boundary current systems including the SCB (Barber and Smith, 1981). Nitrogen,
primarily in the form of nitrate, has been identified in many of these regional studies as the
macronutrient that is limiting, and therefore the macronutrient that governs the patterns of
primary production (Capone and Hutchins, 2013; Eppley et al., 1979; Thomas et al., 1974). By
comparison, the importance of anthropogenic nutrient loading as a factor stimulating primary
production has been less well defined.
Anthropogenic nutrient loading into coastal waters has been implicated as the cause of
overgrowth of phytoplankton, increased occurrence and frequency of harmful algal blooms
(HABs), and other negative ecosystem impacts (Heisler et al., 2008; Howarth, 2008; Rabalais et
al., 2009). Much of the SCB coastline is heavily urbanized and is one of the most densely
populated coastal regions in the United States (Culliton et al., 1990). The central region of the
SCB is comprised of Santa Monica Bay (SMB) and the San Pedro Shelf (SPS) and is the most
urbanized sub-region of the Bight. Five Publicly Owned Treatment Works (POTWs) border the
coastline of SMB and SPS, three of which each discharge >3.8 x 10
8
L day
-1
of secondarily-
treated, nutrient-rich, wastewater effluent (hereby referred to as effluent) into the ocean (Howard
et al., 2014). These ocean outfalls in the central Bight are a constant point source of
anthropogenic nutrient input into this sub-region of the SCB.
132
Nitrogenous inputs from effluent, which are predominately in the form of ammonia, are
approximately equal to upwelling as a source of nitrogen annually in SMB and the SPS (Howard
et al., 2014). Inputs from POTWs differ significantly from upwelling in their nitrogen:
phosphorus (N:P) ratios as well as the form of nitrogen (McLaughlin et al., 2017). The
differences between POTW discharges and natural nutrient sources on primary production in the
region are poorly understood, but changes in phytoplankton community structure and the
frequency of phytoplankton bloom development have been suggested (Howard et al., 2014;
McLaughlin et al., 2017; Nezlin et al., 2012; Reifel et al., 2013). Nezlin et al. (2012) that found
that many phytoplankton blooms in the region were co-located with effluent discharge points
during a 10-year study. Reifel et al. (2013) reported a spatially restricted bloom of several
dinoflagellate taxa in the SMB linked to effluent discharge. Furthermore, several studies in the
region have reported increased chlorophyll a concentrations unrelated to upwelling events,
implying POTW inputs may contribute to phytoplankton blooms in the region. (Corcoran et al.,
2010; Kim et al., 2009; Nezlin et al., 2012),
Coincidentally, blooms of HAB taxa in the central Bight have increased over the last decade
and a half (Kim et al., 2009; Schnetzer et al., 2013; Schnetzer et al., 2007; Seubert et al., 2013;
Shipe et al., 2008; Smith et al., 2018). The specific stimulation of HAB species by effluent
discharge has become a focus of research and monitoring within the Bight during this same
period due to the presence of several noxious and toxic taxa endemic to the region (Caron et al.,
2010; Shipe et al., 2008). The most commonly observed HAB organisms in the region are
species within the diatom genus Pseudo-nitzschia spp., several of which produce the neurotoxin
domoic acid (Smith et al., in press). Other HAB organisms documented in the region include
dinoflagellates of the genera Alexandrium spp. (saxitoxin producer), Dinophysis spp. (okadaic
133
acid producer), Prorocentrum spp. and Lingulodinium polyedra (yessotoxin producer)(Garneau
et al., 2011; Howard et al., 2008; Reifel et al., 2013; Seubert et al., 2013; Shipe et al., 2008;
Stauffer et al., 2012). Species such as Chattonella marina (potential brevetoxin producer),
Heterosigma akashiwo, Margalefidinium fulvescens (formerly Cochlodinium fulvescens), and
some species of marine euglenoids are documented fish-killing species that have also been
observed (Caron et al., 2010; Howard et al., 2012; Reifel et al., 2013; Stauffer et al., 2013).
Modern effluent outfalls that discharge relatively large volumes of effluent have been
designed to discharge below the euphotic zone several kilometers from the shore (Washburn et
al., 1992). The outfalls are sited in part to prevent nutrient-rich effluent from reaching the
euphotic zone, thus minimizing their impact on phytoplankton growth. However, POTWs must
periodically divert effluent flow to alternative outfalls located in much shallower water for
maintenance and repairs of the primary facility. These diversions into shallow water provide
unique opportunities to study the response of planktonic biomass, community structure and
potential stimulation of HAB taxa to effluent discharged directly into the euphotic zone.
We investigated the response of the plankton community to a planned six-week (21
September to 2 November 2015) diversion of effluent by the Hyperion Treatment Plant (HTP) to
its shallow-water near-shore outfall located within the euphotic zone of SMB. The diversion was
necessary for repairs/rehabilitation to the plant’s effluent pumping system. HTP is the largest
POTW in the SCB and discharges roughly 8.5 x 10
8
L day
-1
of secondarily-treated effluent into
SMB through a diffuser located roughly 8.1 km from shore at a depth of roughly 57 m
(depending on tide height). The effluent was diverted to an auxiliary outfall diffuser located
roughly 1.2 km from shore at a depth of roughly 16 m.
134
Biological response to the shallow discharge was monitored with weekly shipboard surveys
conducted prior to, during and after the diversion. In an effort to link biological events to the
temporary shallow discharge, the effluent was tracked from its discharge location with clusters of
water-following drifters, and salinity observations were made following drifter motion.
Chlorophyll a concentrations far exceeding regional bloom thresholds were observed on multiple
surveys during the event. Three taxonomically distinct phytoplankton blooms that were linked to
effluent release were observed, including elevated abundances of several HAB taxa.
3.2 Materials and Methods
3.2.1 Study Site and Sampling Locations
The study was conducted in the coastal waters of Santa Monica Bay, which is a semi-
enclosed bay with an approximate area of 690 km
2
bounded to the northwest by Point Dume and
to the southwest by the Palos Verdes Peninsula (Figure 3.1). Shipboard surveys were conducted
prior to the diversion (n = 1), during the diversion (n = 7), and after the diversion (n = 2) to
monitor the response of theplankton to the input of nutrients from the diverted effluent. The pre-
diversion survey was conducted on 16 September at eight stations (D2W, D3W, D4W, D5W,
D6W, D8W, D9W, and D10W; Figure 3.1) to assess phytoplankton biomass, abundances and
taxonomic composition of the community across the bay prior to the start of the diversion on 21
September. Weekly shipboard surveys during the diversion and after the diversion consisted of
10 stations (D2W, D4W, D5W, D6W, D7D, D8W, D9W, D10W, D11W, and D13W; Figure
3.1). Core stations followed a general numeric scheme where numbers increased from north to
south. Stations were oriented in bands (north to south orientation) of nearshore stations (~1 - 2
km from shore; D5W, D9W, and D13W), ‘mid-nearshore’ stations (~3 - 5 km from shore; D2W,
D4W, D6W, D8W, and D10W) and offshore stations (~8 km from shore; D7W and D11W).
135
Additional stations and surveys were added on an ad hoc basis in response to visual reports of
elevated algal biomass during the diversion. Surveys were conducted on 16, 23 and 30
September, 7, 14, 21 and 28 October, 5 and 11 November with the M/V La Mer, and on 17
October with the M/V Marine Surveyor (Figure 3.1).
3.2.2 Sensed Shipboard Measurements and Water Collection
A vertical profile consisting of sensed environmental parameters (salinity, temperature,
depth) was conducted at each sampling station on surveys conducted on the M/V La Mer using
an SBE 9plus CTD system (Sea-Bird Electronics; Bellevue, WA). Salinity (PSS-78) measured
during these hydrocasts was used to estimate the degree of effluent influence. CTD profiles were
not collected on the 17 October M/V Marine Surveyor cruise. A total of 118 vertical profiles
were obtained.
Discrete water samples were collected using an SBE 32STD water-sampling carousel
equipped with 12, 1.7 l General Oceanics
®
Niskin bottles near the surface (~2 m depth) and
either the subsurface chlorophyll maximum (SCM) if that feature was present, or mid-depth
between the surface and sea floor if no SCM feature was present. Water samples were collected
at the surface using a bucket during the ad hoc survey on 17 October. A total of 183 discrete
water samples were collected during the study.
3.2.3 Tracking the Effluent Plume with Drifters
The horizontal motion of effluent plume and coastal waters was tracked with Microstar
®

drifters (Pacific Gyre Corporation; Carlsbad, CA), and plume water mixing was traced through
changes in salinity following drifter (plume) motion (Ohlmann et al., 2007; Ohlmann et al.,
2005). Drifter data also provided an opportunity to link water masses originating at the outfall to
the times/locations of observed phytoplankton blooms. Drifters recorded their position every 10
136
minutes with GPS and transmitted data to a host computer via an Iridium satellite
communications network. The sampling frequency provided a high signal-to-noise ratio even in
low-velocity regimes. The spatial accuracy and near real-time transmission enabled drifters to be
located, recovered and redeployed during the course of an experiment.
Drifter sampling involved the repetitive deployment of 4 to 6 drifters just above the
effluent diffuser. Each drifter cluster was deployed in a rectangular grid configuration with
roughly 50 m spacing. Drifters were deployed in the morning and left in the water for roughly 4
to 6 hours. In many instances, a mid-day deployment with 4-hour sampling also occurred. In
some cases (when beaching did not appear imminent) drifters were left to sample for between 24
and 72 hours. The drifter deployments occurred on 23 days from 16 September to 2 November.
Drifter deployments did not necessarily correspond with the timing of the shipboard surveys
described above. A total of 178 drifter trajectories were collected during the study.
3.2.4 Plankton Community Composition and Chlorophyll a
Phytoplankton and microzooplankton community composition analysis was conducted
following the Utermöhl method (Utermöhl, 1958) with a Leica DM IRBE inverted light
microscope (Leica Microsystems; Buffalo Grove, IL). Seawater was transferred into 125 ml
glass bottles, preserved with 1% formaldehyde (final concentration), and stored at 4 °C until
analysis. The counting method yielded a detection limit of 1000 - 3000 cells L
-1
depending on
the volume settled and the number of fields counted. Cells were categorized into four groups:
diatoms, dinoflagellates, microzooplankton, and ‘other’, which included euglenoids,
raphidophytes, and silicoflagellates. Organisms within each group were identified to genus
whenever possible.
137
Heterotrophic bacteria (bacteria + archaea), picoplanktonic cyanobacteria
(Synechococcus and Prochlorococcus), and picoeukaryotic algae were collected for enumeration
via flow cytometry. Samples for flow cytometry were pre-filtered through a 20µm Nitex mesh,
preserved with 0.24% paraformaldehyde (final concentration) and flash frozen at -80 °C until
analysis. Samples were analyzed on a FACScalibur flow cytometer (Becton Dickinson; San Jose,
CA). Picoplanktonic cyanobacteria and picoeukaryotic algae were measured based on the
autofluorescence of photosynthetic pigments and forward scatter. Heterotrophic bacteria were
measured following standard staining protocols described in Giorgio et al. (1996).
Chlorophyll a (Chl a) was measured fluorometrically using the non-acidification method.
Samples were collected for chl a analysis by gentle filtration of 5-100 ml of sample onto glass
fiber filters (Whatman GF/F), depending on the phytoplankton biomass in the sample. Filtration
volume was varied according to information on chl a fluorescence provided by the in situ
instrument. Filters were extracted in 100% acetone at -20 °C in the dark for 24 hours. Acetone
extracts were analyzed on a Trilogy Turner Designs fluorometer (Turner Designs; Sunnyvale,
CA).
3.2.5 Dissolved Inorganic Nutrients
Seawater samples were collected to measure dissolved NH
3
-N, NO
3
-
-N, PO
4
3-
and SiO
4
2-
.
Samples for NH
3
-N, NO
3
-
-N, PO
4
3-
were collected at every station and depth, except on the ad
hoc 17 October survey. NH
3
-N and PO
4
3-
were collected and analyzed as described in Otim et al.
(2018). Briefly, NH
3
-N was analyzed according to USEPA 1993a, method 350.1 with semi-
automated colorimetry. PO
4
3-
concentrations were measured spectrophotometrically following
the standard method 4500-PE (APHA 2012a). NO
3
-
-N was determined via automated
colorimetry following USEPA 1993c, method 353.2.
138
Seawater was filtered through 0.45 µm acetate luer-lock syringe filters and the filtrate
was collected for dissolved SiO
4
2-
analysis. The filtrates were stored at 4 °C until analysis.
Samples were analyzed spectrophotometrically according to the methods described in Mullin and
Riley (1955). Only a subset of stations and dates were analyzed for SiO
4
2-
due to limited
resources.
3.2.6 Chlorination of Effluent Discharged During the Diversion
Disinfection of the discharged effluent via chlorination was conducted during the
diversion to comply with permitting requirements and minimize public health risks of nearshore
discharge. Total chlorine residual (TCR) was tested daily in the treated effluent prior to
discharge through the 1.2 km outfall. TCR was measured by N, N-diethyl-p-phenylenediamine
(DPD) colorimetry following standard method 4500-Cl G (APHA 2012a). Detailed sample
collection and processing protocols are described in Otim et al. (2018).
3.2.7 Measurements of Particulate Algal Toxins
Samples were collected for particulate domoic acid and particulate brevetoxin analysis
via gentle vacuum filtration of 200 ml and 400 ml of sample water, respectively, onto glass fiber
filters. All filters were stored at -20 °C and kept in the dark until analysis. Filters were extracted
for domoic acid analysis in 3 ml of 10% methanol, sonicated for 15s, and centrifuged for 15 min
at 4,000 rpm. The supernatant was analyzed using ELISA (Mercury Science; Durham, NC)
according to the methods described in (Litaker et al., 2008).
Filters collected for brevetoxin analysis were cut in half and one half was extracted in
80% MeOH and the other half in 100% MeOH. Each extraction used 250 µl of solvent. Filters
were then cycled through three freeze-thaw cycles, agitated thoroughly with a glass rod and then
passed through a syringe filter. The filtrate of both extracts was analyzed on an Agilent 1260 U-
139
HPLC (Agilent Technologies; Santa Clara, CA) with an isocratic 85:15 MeOH:H
2
O mobile
phase through a C18 reverse phase column. An Agilent 1260 Diode Array Detector at 215 nm
was used for detection.
3.2.8 Statistical Analyses
Relationships between chl a and other seawater variables sampled concurrently during
weekly surveys (temperature, salinity, NH
3
, NO
3
-
, PO
4
3-
) were examined with Spearman rank
order correlation analysis using SigmaPlot (v.13.0.0, Systat Software, Inc.). The ad hoc 17
October survey data were limited to chl a and plankton community composition and therefore
were excluded from the analysis. Dissolved SiO
4
2-
was also excluded from the analyses due to
the limited number of observations. The analysis was performed using only data collected during
the diversion, and a second time using all data including those collected during the pre- and post-
diversion surveys. Significant positive or negative Spearman’s correlation coefficient (ρ) were
defined at p < 0.05.
Differences in the plankton community structure observed during phytoplankton blooms
were analyzed using the software package PRIMER v6 (Clarke and Gorley, 2006). Community
structure analyses were conducted on samples where chl a concentration was ≥10 µg L
-1
.
Microplankton abundance data from microscopical counts were square root transformed and then
used in Bray-Curtis similarity calculations. Hierarchical clustering analysis (CLUSTER) was
conducted with the results of the Bray-Curtis calculations to determine the similarity between
plankton community compositions. Similarity profile permutation tests (SIMPROF) were
conducted to determine if dissimilarities among communities were significant (p < 0.05).

140
3.3 Results
3.3.1 Conditions in Santa Monica Bay Prior to the Diversion
A large, unseasonal rain event (~ 5 cm over ~ 6 hours) occurred on 15 September, roughly
one week prior to the planned diversion, resulting in stormwater intrusion into the sewer system and
necessitating an emergency bypass discharge of 1.1 x 10
8
l of effluent through the 1.2 km outfall
pipe (Otim et al., 2018). The rain event added freshwater to the nearshore coastal ocean; directly,
through runoff, and via additional discharged effluent. The rain event presumably introduced some
undocumented amount of nutrients into the ocean near the region of the 1.2 km outfall but it is
unlikely that the phytoplankton community could have responded to that input in the one-day period
between the rain event and the pre-diversion survey.
Chlorophyll a (chl a) concentrations were low (0.32 µg L
-1
- 1.76 µg L
-1
) across all
stations and depths sampled during the pre-diversion survey. These values are typical of non-
bloom conditions in the region (Shipe et al., 2008) (Figure 3.2, 3.3A). Total microplankton
abundances ranged from 6 x 10
3
cells L
-1
to 9 x 10
4
cells L
-1
(Figure 3.4A) and the plankton
community was generally dominated by pennate diatoms (Cylindrotheca and Navicula) and
microzooplankton (ciliated protists). Heterotrophic bacterial abundances ranged from 8 x 10
8

cells L
-1
to 2 x 10
9
cells L
-1
(Figure 3.4B). Cyanobacterial (Synechococcus and Prochlorococcus)
abundances ranged from 1 x 10
7
cells L
-1
to 2 x 10
8
cells L
-1
(Figure 3.4C). Picoeukaryotic algal
abundances ranged from 6 x 10
6
cells L
-1
to 2 x 10
7
cells L
-1
(Figure 3.4D). Surface water
temperature during the pre-diversion survey ranged from 23.2 °C to 24.6 °C and salinity ranged
from 32.50 psu to 33.24 psu (Table 3.1). The lowest salinity values (≤ 33.00 psu) were observed
at the nearshore stations and likely resulted from rainwater runoff and the emergency stormwater
discharge from the 1.2 km outfall pipe on 15 September. NH
3
-N, NO
3
-
-N, PO
4
3-
concentrations
141
were generally below the detection limits of the respective methods (Table 3.1).
3.3.2 Characteristics and Movement of Effluent During the Diversion
Upon discharge from the diffuser on the ocean floor, effluent was driven upward by
momentum and buoyancy forces. As the fresh effluent plume surfaced, its salinity steadily
increased due to mixing with the salty ambient ocean water. The upward vertical movement of
the effluent also entrained cool bottom water toward the sea surface. The effluent discharged from
the shallow outfall quickly reached the sea surface and was clearly visible as a surface ‘boil’ (Trinh
et al., 2017). Surface waters directly above the outfall (Station D9W) showed distinct temperature
and salinity (T-S) characteristics throughout the diversion (Figure 3.5). Surface salinity at Station
D9W was generally more than 1 psu fresher than at other stations (values ≤ 32.00 psu versus values
≥ 33.00 psu at stations away from the outfall). Surface water temperatures were also cooler and
showed relatively little variability at Station D9W compared with other stations (Figure 3.5, Table
3.1).
Water parcels that consisted of mixtures of relatively freshwater effluent and relative
salty ocean water were identified from their lower salinity compared with background ocean
waters unimpacted by effluent. The largest chl a concentration were typically observed in samples
with somewhat low salinities, indicating the presence of freshwater effluent (Figure 3.5). However,
low salinity observations were not necessarily coincident with elevated chl a concentration. This
was particularly true at nearshore stations during the pre-diversion survey following the 15
September rain event. The average surface salinity computed from observations (excluding the
outfall location, i.e. Station D9W) during the diversion was 33.30 ± 0.16 psu. Surface salinities
associated with high chl a concentrations (>10 µg L
-1
) were always ≤33.20 psu, or more than 0.1
142
psu below the mean. Overall, surface values of chl a and salinity showed a statistically significant
negative correlation during the diversion (Table 3.2).
High concentrations of NH
3
-N and PO
4
3-
were detected at Station D9W (above the 1.2 km
outfall) throughout the diversion whereas NO
3
-N, NH
3
-N and PO
4
3-
concentrations were usually
below the detection limits of the methods throughout the rest of the sampling grid. NH
3
-N and PO
4
3-
concentrations ranged from 95.0 µM to 206 µM and from 3.23 µM to 5.17 µM, respectively at
Station D9W throughout the study, and were also elevated in localized regions of recent plume
impact (see Otim et al., 2018) (Table 3.1). Nutrients were generally a poor tracer of the presence of
effluent plume due to biological uptake, but chl a concentrations were weakly positively
correlated with NH
3
-N during the diversion but not PO
4
3-
or NO
3
-
-N (Table 3.2).
The level of effluent chlorination was inconsistent during the diversion. Total chlorine
residual measured in the effluent prior to discharge into the bay ranged from ≤0.05 mg L
-1
detection
to 3.8 mg L
-1
during the diversion (Supplementary Figure 3.1). Total chlorine residual was ≤0.05
mg L
-1
for 11 days of the diversion, during three different time spans: 6 – 8 October, 14 – 20
October and 22 October (see Otim et al., 2018).
Drifter observations (Figure 3.6) indicated plume water movement primarily upcoast
(northward) and onshore (not shown). Net offshore drifter (plume) motion, where the ending
position of a drifter track was seaward of the diffuser, was rarely observed. Surface current
velocities generally ranged from 5 to 15 cm s
-1
. The largest velocities observed were nearly 35
cm s
-1
and appeared late in the day when the local onshore sea breeze was relatively strong. The
observed flow patterns were consistent with known regional circulation forced primarily by local
winds (Csanady, 1972; Fewings et al., 2008). Surface current oscillations near tidal frequencies
were seen in the longest drifter tracks (Figure 3.6C). These flow reversals suggested plume
143
waters could remain within SMB for many days. In some cases, parcels of water received
multiple injections of effluent emerging from the outfall. That is, a water parcel could move over
the outfall location multiple times. In general, the drifter observations connected plume waters
tagged at the outfall to the locations and times of the three large phytoplankton blooms that
occurred during the diversion (Figure 3.6; further discussed below).
3.3.3 Phytoplankton Bloom Definition and Identification
Phytoplankton blooms were identified based on regional chl a bloom threshold
definitions developed from time series phytoplankton monitoring programs conducted in the
Southern California Bight. Thresholds derived from a two-year chl a time series from Redondo
Beach Pier in Redondo Beach, CA, located within SMB, defined a chl a concentration of ≥10 µg
L
-1
as minor phytoplankton bloom, and a chl a concentration of ≥13 µg L
-1
as a major
phytoplankton bloom (Seubert et al., 2013). These thresholds are similar to thresholds developed
from a longer, eighteen year chl a time series from Scripps Pier in La Jolla, CA, south of the
study region (Kim et al., 2009). In the latter study, thresholds of ≥5.7 µg L
-1
chl a were defined
as a minor bloom and ≥18 µg L
-1
chl a as a major bloom. Based on the regionally specific criteria
from Redondo Beach pier, three major phytoplankton blooms were identified during the
diversion (Figure 3.2, 3.3). The blooms were observed on 30 September (Bloom 1, Figure 3.3C),
14 and 17 October (Bloom 2, Figure 3.3E, 3.3F), and 21 October (Bloom 3, Figure 3.3G).
Maximal chl a concentrations during the three bloom events greatly exceeded all major bloom
thresholds (4- to 15-fold), with observed surface chl a concentrations ranging from 48.6 µg L
-1
to
>195 µg L
-1
. Chl a concentrations that exceeded the minor bloom threshold (but were below the
major threshold) were observed at one station (D2W) on 28 October (Figure 3.3H) and at one
station (D13W) after the diversion on 5 November (Figure 3.3I). These latter events were distinct
144
from the three major blooms indicated above and did not develop into major blooms. Chl a
concentrations above bloom thresholds were restricted to the very nearshore region of the bay.
Low (≤3.39 µg L
-1
)

chl a concentrations were observed at both surface and subsurface depths at
the offshore stations (Stations D7W and D11W) throughout the study (Figure 3.2, 3.3).
Extremely high chl a concentrations were never observed directly above the outfall (Station
D9W). Chl a concentrations at the outfall were ≤5.47 µg L
-1
at the surface and ≤7.54 µg L
-1

subsurface

throughout the diversion.
3.3.4 Bloom 1
The first major phytoplankton bloom was detected during the 30 September shipboard
survey, nine days after the start of the diversion, and was spatially confined to the nearshore
Stations D5W and D13W (Figure 3.3C). The most intense region of the bloom was located at
Station D13W just offshore from the City of Hermosa Beach (Figure 3.2). Chl a concentrations
at D13W were 48.7 µg L
-1
at the surface and 29.7 µg L
-1
at depth. High phytoplankton biomass
was also detected at Station D5W located offshore of Marina Del Rey, where chl a
concentrations were 21.5 µg L
-1
at the surface and 14.9 µg L
-1
at depth (Figure 3.2). In contrast,
chl a concentrations at the 1.2 km outfall (Station D9W) during this survey were only slightly
elevated compared to pre-diversion conditions with chl a concentrations of 4.04 µg L
-1
at the
surface and 4.25 µg L
-1
at depth (Figure 3.2). Chl a concentrations in the rest of the bay were
much lower with values ranging from 0.24 µg L
-1
to 2.57 µg L
-1
(Figure 3.3C).
Total microplankton abundances at Stations D5W and D13W where the bloom on 30
September was located ranged from 2 - 4 x 10
6
cells L
-1
(Figure 3.4A). Diatoms comprised
between 79% and 96% of the total microplankton within the bloom. The community was
comprised of several genera of chain-forming diatoms, the most abundant of these taxa were
145
members of the genera Chaetoceros and Leptocylindrus. Cell abundances at D9W were 6 x 10
5

cells L
-1
, about an order of magnitude lower than that observed in bloom samples. Mid-offshore
stations (D2W, D4W, D6W, D8W, and D10W) had diatom-dominated communities similar to
the bloom locations, but with overall lower cell abundances (2 x 10
4
cells L
-1
to 5 x 10
5
cells L
-1
).
The offshore stations (D7W and D11W) also had low abundances of microplankton (≤2 x 10
4

cells L
-1
; Figure 3.4A).
The potentially toxic diatom genus Pseudo-nitzschia spp. was present in 50% of the
samples collected during the 30 September survey, but at relatively low abundances (<2.6 x 10
4

cells L
-1
). Domoic acid was below detection in toxin analyses conducted on all samples collected
from the sample grid on the day of the bloom. The potential HAB-forming raphidophytes,
Heterosigma akashiwo (<2 x 10
5
cells L
-1
), Chattonella marina (<3 x 10
4
cells L
-1
), and the
dinoflagellate Prorocentrum spp. (<1 x 10
5
cells L
-1
) were also detected in this bloom, but were
subdominants to diatoms.
Abundances of heterotrophic bacteria and microzooplankton were elevated at the same
stations where diatom abundances were high on 30 September (D5W and D13W; Figure 3.3C,
3.4B, Supplementary Figure 3.2), while cyanobacterial abundances were only elevated at the
stations away from the bloom regions. Heterotrophic bacterial abundances were 2 - 5 x 10
9
cells L
-1
(Figure 3.4B) at the surface at the bloom stations and also at the surface at the 1.2 km outfall
station. Heterotrophic bacterial abundances outside the bloom regions, and away from the 1.2 km
outfall were lower (5 x 10
8
cells L
-1
to 1 x 10
9
cells L
-1
) (Figure 3.4B). Cyanobacterial abundances
were generally higher in the subsurface than at the surface at all stations, particularly at the offshore
Station D7W. Overall, cyanobacterial abundances ranged from 1 x 10
7
cells L
-1
to 1 x 10
8
cells L
-1
during the 30 September survey (Figure 3.4C). Picoeukaryotic algal abundances were similar to
146
abundances observed prior to the diversion and ranged from 3 x 10
6
cells L
-1
to 1 x 10
7
cells L
-1

(Figure 3.4D). Abundances of microzooplankton were elevated at bloom stations (~3 x 10
4
cells L
-
1
), but abundances away from these stations were more similar to pre-diversion observations
(Supplementary Figure 3.2).
All 11 drifters deployed on 28 September measured downcoast flow (Figure 3.6A). The
three drifters that sampled for more than a few hours abruptly changed direction (toward
onshore) on 29 September (Figure 3.6A). Two drifter tracks terminated a few kilometers from
Station D13W, the site of the diatom bloom. These observations suggest effluent waters (as
tagged by drifters) reached the bloom site roughly one day prior to the elevated chl a
observations. Surface salinity measured coincidently with chl a at Station D13W supported the
existence of plume waters in the region at the time of the bloom. D13W surface salinity on 30
September (33.05 psu) was more than 0.25 psu less than (fresher) than the mean value computed
from all D13W salinity data collected during the diversion. A connection between effluent and
the elevated chl a signal at Station D5W was not evident in the drifter data. Salinity at D5W
during the bloom (33.18 psu) was larger than at D13W, but still 0.10 psu less than (fresher) than
the mean value for the station.
3.3.5 Bloom 2
The second major phytoplankton bloom was observed on 14 October, 26 days after the
beginning of the diversion, at Stations D2W and D5W, geographically extending northward from
Marina del Rey Harbor (Figure 3.2, 3.3E). Elevated chl a was restricted to the surface with
concentrations of 13.6 µg L
-1
of chl a at D2W and 20.7 µg L
-1
at D5W. Subsurface chl a
concentrations were below bloom thresholds, 3.02 µg L
-1
and 3.98 µg L
-1
at D2W and D5W,
respectively (Figure 3.3E). Outside the bloom area to the south, phytoplankton biomass was
147
consistently below bloom thresholds, with chl a concentrations ranging from 0.29 µg L
-1
to 4.69
µg L
-1
. Chl a concentrations were generally higher in the northern portion of the sampling area
than in the south and greatly reduced at mid-offshore and offshore stations (Figure 3.3E).
The bloom at D2W and D5W was dominated by marine euglenoids. Euglenoid cell
abundances at D5W and D2W comprised 92% (2 x 10
6
cells L
-1
) and 72% (7 x 10
5
cells L
-1
) of
the total microplankton community, respectively (Figure 3.4A). Abundances of C. marina were
also notably elevated at these stations with both stations having abundances of ~1 x 10
5
cells L
-1
.
The phytoplankton community composition showed a gradient from north to south, with the
bloom stations in the northern region of the bay dominated by marine euglenoids and C. marina,
and the other stations in the southern region of the bay dominated by chain-forming diatoms,
mainly of the genera Chaetoceros and Leptocylindrus.
Heterotrophic bacteria were elevated in surface waters at the northern Stations D2W,
D4W, D5W, and at the 1.2 km outfall on 14 October (Figure 3.4B). Abundances at these stations
ranged from 2 - 4 x 10
9
cells L
-1
.

Bacterial abundances were lower at the offshore and southern
stations and ranged 7 x 10
8
cells L
-1
to 1 x 10
9
cells L
-1
(Figure 3.4B). In contrast, cyanobacterial
abundances were high at the offshore Station D11W, particularly in the subsurface where
abundances were ~2 x 10
8
cells L
-1
. Ranges of cyanobacterial abundances were lower at the
nearshore stations (1 - 7 x 10
7
cells L
-1
; Figure 3.4C). Abundances of picoeukaryotic algae and
microzooplankton were relatively constant among stations and were generally within the range of
abundances observed prior to the diversion (Figure 3.4D, Supplementary Figure 3.2).
An additional survey was conducted on 17 October to further investigate the marine
euglenoid bloom (Figure 3.2, 3.3F). A range of chl a concentrations from 18.1 µg L
-1
to >195 µg
L
-1
(upper detection limit for our method) was detected across the 5 stations sampled (diamonds
148
in Figure 3.1, 3.3F), with the highest chl a concentrations at Stations 1, 3 and 4. Euglenoid cell
abundances at those stations ranged from 1 x 10
6
cells L
-1
to 1 x 10
8
cells L
-1
(Figure 3.4A).
Abundances of heterotrophic bacteria, cyanobacteria and picoeukaryotic algae were also
highest at Stations 1, 3 and 4 (i.e. where chl a concentrations were maximal). Overall, the
heterotrophic bacterial abundances observed on 17 October were the highest observed
throughout the study, ranging from 2 - 5 x 10
9
cells L
-1
(Figure 3.4B). Abundances were highest
at Station 4 where euglenoid abundances were highest. Similarly, abundances of cyanobacteria
and picoeukaryotic algae were highest at Stations 1, 3 and 4 (Figure 3.4C,D). Cyanobacterial
abundances ranged from 9 x 10
6
cells L
-1
to 9 x 10
7
cells L
-1
(Figure 3.4C). Picoeukaryotic algal
abundances ranged almost two orders of magnitude from 8 x 10
6
cells L
-1
to 1 x 10
8
cells L
-1
(Figure 3.4D). Total abundances of microzooplankton at station 4 were the highest observed during
the study (~3 x 10
5
cells L
-1
), but overall microzooplankton abundances ranged several orders of
magnitude across sampling stations (Supplementary Figure 3.2).
Drifters deployed on 14 and 16 October, just prior to and during the large euglenoid
bloom, revealed generally upcoast flow from the outfall toward the high chl a stations (Figure
3.6B,C). These drifters sampled surface current velocities of ~5 to 15 cm s
-1
for up to ~6 hours
but were recovered well before reaching the bloom region. Nonetheless, the drifter observations
showed the general flow direction necessary for plume waters to reach the locations with high
chl a concentrations on 14 and 17 October. Surface salinity measured coincidently with chl a at
Station D5W suggested the existence of plume waters in the region at the time of the bloom.
Surface salinity measured at Station D5W on 14 October (32.91 psu) was 0.18 psu less than
(fresher) than the mean value for the diversion at that station. This was a lower salinity value
than recorded at this station (D5W) during the 30 September bloom (Bloom 1 discussed above;
149
33.18 psu) suggesting a greater concentration of effluent at D5W during the larger Bloom 2
event.
3.3.6 Bloom 3
A third distinct phytoplankton bloom was detected on 21 October, 30 days after the
beginning of the diversion, in King Harbor, City of Redondo Beach, and at Station D13W (same
location as Bloom 1)(Figure 3.2, 3.3G). Chl a concentration was 53.9 µg L
-1
within King Harbor.
Outside the harbor at Station D13W chl a was 31.2 µg L
-1
and 8.47 µg L
-1
at the surface and

subsurface, respectively (Figure 3.3G). Chl a concentrations were lower at stations north of
D13W and ranged from 0.45 µg L
-1
to 7.45 µg L
-1
at surface and subsurface depths, respectively
(Figure 3.3G). Cell abundances at these stations ranged from 1 x 10
4
cells L
-1
to 1 x 10
5
cells L
-1
and diatoms were numerically dominant at the non-bloom stations (Figure 3.4A).
The bloom within King Harbor and at Station D13W was dominated by the raphidophyte,
C. marina. High abundances of C. marina (~8 x 10
5
cells L
-1
) were observed within King Harbor
and at Station D13W (5 x 10
5
cells L
-1
and 1 x 10
5
cells L
-1
in surface waters and subsurface,
respectively). Samples were collected at a Station DD17B at the southern end of SMB (Figure
3.1) to determine the southern extent of the bloom. Abundances of C. marina at DD17B were
two orders of magnitude lower than in King Harbor. Brevetoxin was not detected in any samples
where C. marina was present.
Heterotrophic bacteria and cyanobacteria were elevated at almost every station during the
21 October survey compared to other surveys. Heterotrophic bacterial abundances were
particularly high at D5W, D9W, D13W and in King Harbor (Figure 3.4B). Abundances of
heterotrophic bacteria were 4 - 8 x 10
9
cells L
-1
at those stations, while abundances were lower
(≤3 x 10
9
cells L
-1
) at all other stations surveyed (Figure 3.4B). Cyanobacterial abundances
150
ranged from 3 x 10
7
cells L
-1
to 1 x 10
8
cells L
-1
(Figure 3.4C) and abundances were lowest in
King Harbor where the C. marina bloom was most intense. Unlike the other picoplanktonic
groups, picoeukaryotic algal abundances were not notably elevated and ranged from 7 x 10
6
cells
L
-1
to 2 x 10
7
cells L
-1
(Figure 3.4D). Overall abundances of microzooplankton were also not
elevated above pre-diversion observations, except at Station D13W and D10W were abundances
were ≥7 x 10
4
cells L
-1
(Supplementary Figure 3.2).
The drifters deployed at the outfall on 19 October generally moved downcoast and
onshore toward the bloom region (Figure 3.6D). As with the Bloom 2 observations, drifters were
recovered before reaching the bloom at Station D13W and King Harbor. A single drifter
deployed on 18 October came within a few kilometers of the bloom location on 20 October, but
then moved rapidly upcoast (Figure 3.6D). Surface salinity measured at Station D13W on 21
October was relatively fresh (33.20 psu), implicating the presence of plume water at the time of
the bloom. This salinity value was greater than measured in conjunction with the 30 September
bloom at this station (33.05 psu), but still 0.10 psu less than (fresher) than the mean value for
Station D13W during the diversion.
3.3.7 Blooms Observed During the Diversion were Taxonomically Distinct
Hierarchical clustering analysis of the microplankton communities of the samples with
≥10 µg L
-1
of chl a (indicative of a minor bloom or greater) was conducted to examine the
relative similarity between bloom communities. Bloom 1 and the minor blooms observed on 28
October and 5 November were diatom-dominated communities and clustered distinctly from
Blooms 2 and 3 (Figure 3.7). However, the communities present during Bloom 1, and the two,
spatially restricted minor blooms were significantly different from each other. Bloom 2 was
surveyed twice, on 14 October and then again on 17 October. The communities observed during
151
each survey of Bloom 2 clustered together, and were distinct from the other communities
associated with high chl a (Figure 3.7). Bloom 3 clustered distinctly and communities were
similar across sampling stations despite the bloom being observed outside and inside of King
Harbor (Figure 3.7).
3.3.8 Conditions in Santa Monica Bay Following the Diversion
Chl a concentrations on 5 November, three days after the diversion, ranged from 0.47 µg
L
-1
to 11.2 µg L
-1
(Figure 3.3I) indicating that chl a at some stations remained elevated compared
with pre-diversion observations. In particular, Stations D6W, D8W, D10W and D13W had chl a
concentrations >5.00 µg L
-1
(Figure 3.1, 3.3I). Chain-forming diatoms dominated the
phytoplankton community at all stations sampled on 5 November. The most common genera were
Thalassiosira, Chaetoceros, and Pseudo-nitzschia. Pseudo-nitzschia spp. were detected in all
samples collected and ranged in abundance from 4 x 10
4
cells L
-1
to 3 x 10
5
cells L
-1
(~9 – 62%
of the total microplankton community). Domoic acid analysis showed low toxin concentrations
in 15% of samples. At Station D6W, particulate domoic acid was 0.05 µg L
-1
and 0.04 µg L
-1
in
surface and subsurface waters, respectively. Particulate domoic acid was 0.06 µg L
-1
at depth at
Station D8W.
Abundances of heterotrophic bacteria, cyanobacteria, and picoeukaryotic algae were
relatively homogeneous among stations on 5 November. Heterotrophic bacteria were lower in
abundance at the more coastal stations compared to surveys conducted during the diversion (5 x 10
8

cells L
1
- 1 x 10
9
cells L
-1
) (Figure 3.4B). Cyanobacterial abundances were similar among all
stations, ranging from 1 - 5 x 10
7
cells L
-1
, with abundances generally higher in subsurface
samples

(Figure 3.4C). Similarly, picoeukaryotic algal abundances showed little variation between
stations (4 - 9 x 10
6
cells L
-1
)

(Figure 3.4D). Microzooplankon abundances were a generally similar
152
to pre-diversion observations on 5 November except at Station D13W, where elevated chl a
concentrations were also observed (Supplementary Figure 3.2).
Post diversion observations on 11 November, nine days after flow ceased from the 1.2
km outfall, showed chl a concentrations between 0.52 µg L
-1
and 2.84 µg L
-1
, generally within
the pre-diversion range (Figure 3.2, 3.3J). The largest chl a concentrations were observed at the
most nearshore stations (D9W and D13W). Similar to 5 November, chain-forming diatoms
dominated the phytoplankton community and Thalassiosira, Chaetoceros or Pseudo-nitzschia
were the most common diatom genera observed. Pseudo-nitzschia spp. cells were observed at
every station, however, domoic acid concentrations were below detection for all stations and
depths.
Heterotrophic bacteria, cyanobacteria, and picoeukaryotic algae largely overlapped in
abundances on 11 November compared to observations on 5 November, and were comparable to
pre-diversion levels. Heterotrophic bacterial abundances ranged from 6 x 10
8
cells L
1
to 2 x 10
9

cells L
-1
(Figure 3.4B). Cyanobacterial abundances were generally higher in the offshore stations,
particularly in the subsurface. Offshore cyanobacterial abundances (7 - 8 x 10
7
cells L
-1
) were
higher than at the nearshore stations (2 - 5 x 10
7
cells L
-1
)

(Figure 3.4C). Picoeukaryotic algal
abundances showed little variation among stations and ranged from 4 x 10
6
cells L
1
to 1 x 10
7
cells
L
-1
(Figure 3.4D). Microzooplankon abundances at Station D13W were elevated, similar to the
observations on 5 November, but were generally found at pre-diversion abundances at all other
stations (Supplementary Figure 3.2).

153
3.4 Discussion
The HTP diversion provided a unique opportunity to assess the response of the plankton
community in SMB to a prolonged discharge of nutrient-rich effluent directly into the euphotic
zone of a nearshore ecosystem. The timing of the diversion allowed a study of the response of
the community in a season generally not influenced by upwelling events. Phytoplankton
dynamics in southern California are generally driven by nutrient inputs from upwelling (Kudela
et al., 2005), which is most prevalent in the spring. This study was conducted in autumn when
nutrient inputs from upwelling in the central Bight are minimal (Hickey, 1992). Coastal winds
were generally light during the diversion period. Nutrient input can also occur through coastal
runoff associated with rain events. While there were unseasonally heavy rains just prior to the
diversion, no measurable rainfall occurred during the diversion.
Approximately 2.7 x 10
6
moles NH
3
-N day
-1
(~8.5 x 10
8
L day
-1
effluent was discharged over
42 days

with an average NH
3
-N concentration of 3.2 mM) were discharged from the 1.2 km
outfall during the study. This discharge resulted in the development of three taxonomically
distinct phytoplankton blooms that, while geographically constrained, far exceeded regional chl
a maxima and bloom thresholds (Kim et al., 2009; Reifel et al., 2013; Seubert et al., 2013; Shipe
et al., 2008). The blooms were generally observed in the most nearshore stations but occurred
both upcoast and downcoast from the discharge location.
3.4.1 Relationship of the Effluent Plume to Bloom Events
Bloom development during the diversion was presumably a result of three interrelated
conditions: (1) nutrient enrichment from effluent ; (2) transport and/or retention of effluent-
enriched water within a limited geographic location; and (3) sufficient time for nutrient uptake
and utilization by phytoplankton cells. When these elements aligned, phytoplankton biomass
154
increased to up to an order of magnitude above bloom conditions. High chl a concentrations
were observed in relatively fresh water, which indicated cells had exposure to effluent inoculated
water to support bloom development. This relationship was extremely apparent at Station D13W
where the coefficient of determination associated with the (linear least squares) regression
between chl a concentration and salinity data for Station D13W was 0.98 (Supplementary Figure
3.3). Two observations of very high chl a concentrations at this station (30 September and 21
October) were associated with commensurately low salinities. Despite relatively few degrees of
freedom (n = 6), the strong statistical relationship suggests that plume waters likely played a
major role in bloom formation via nutrient input.
The relationship between chl a and salinity at Station D5W, the other station that
experienced large blooms during the diversion, was not as apparent. Relatively low salinity
values were observed during blooms at that station, but similarly low salinity values (<33.05
psu) were also observed on sampling dates without blooms (chl a <4.0 µg L
-1
). Station D5W was
located just offshore of the Ballona Wetlands, one of the largest freshwater marshes in Los
Angeles County. It is possible that these low-salinity, non-bloom situations may be a
consequence of freshwater entering the ocean near D5W from the marsh, lowering the salinity
but without a large influx of nutrients. An equally likely explanation is that there was insufficient
time for bloom development on some sampling dates.
The retention of the effluent in the nearshore region of discharge for extended periods
likely minimized dilution of nutrients, relative to biological uptake. This ultimately allowed
sufficient time for bloom development. Uchiyama et al. (2014) reported that model simulations
of effluent discharge from the shallow water outfall in SMB showed relatively weak dilution and
retention of the plume within the nearshore regions of the bay. Similarly, low salinity plumes in
155
the nearshore regions of SMB due to urban runoff of storm water after rain events were found to
disperse slowly, and have retention times of at least two to five days (Corcoran et al., 2010).
Tracks for drifters support the nearshore retention of the effluent plume during the
diversion. This plume-tracking component of the diversion monitoring was focused on effluent
advection and dilution during the 4 to 6 hours after discharge, but a few drifters remained
deployed for up to three days. One drifter deployed on 18 October was transported to the vicinity
of Station D13W on 20 October, but then reversed direction and moved back to its point of
origin (Figure 3.6D). While only a single track, it does nonetheless illustrate two things. First,
weak and oscillatory surface currents that enhance residence times do occur in the region.
Second, water parcels originating at the outfall (and containing plume water) can move back
over the outfall to be ‘re-inoculated’ with effluent. This ‘re-inoculation’ or injection of effluent
into a water mass that already contains some effluent would significantly increase both nutrient
availability and the length of time over which elevated nutrient concentrations exist in a specific
water mass. This mechanism is believed to be a potentially important factor in the development
of the blooms observed during the diversion.
Nutrient input during the diversion did not result in a spatially uniform increase in chl a
throughout SMB or within some region centered on the outfall location. Instead, the distribution
of blooms was temporally and spatially patchy. Blooms were quite spatially restricted, almost
always observed within a kilometer or two from the shore. Only slight elevations of chl a were
observed directly about the outfall where plume impacts were strongest. This was likely due to
movement of the effluent plume away from the outfall at most times, as demonstrated by drifters
(Figure 3.6), which did not allow the phytoplankton assemblage sufficient time to utilize the
nutrients locally. As a result, large blooms did not develop over the outfall. Additionally, the fact
156
that chl a concentrations in the more offshore regions of SMB remained low throughout the
study is consistent with the observed plume motion and the lack of drifter tracks showing
offshore movement of plume water. Relative high salinity values at the offshore stations
corroborate a small influx of effluent at those stations.
3.4.2 Diverted Effluent Stimulated Blooms of Distinct Phytoplankton Taxa
A mixed assemblage of diatoms dominated the first large bloom (Bloom 1) detected during
the diversion on 30 September. Geographically, this diatom bloom was found in two distinct
nearshore regions, which were the most nearshore stations upcoast and downcoast of the outfall
(D5W and D13W, respectively; Figure 3.1, 3.3C). The communities in these regions were ~60%
similar in composition. The diatom bloom was most intense at Station D13W where the effluent
concentration, as inferred from salinity, was largest. The plume impact was less apparent at
Station D5W, upcoast of the outfall, where the salinity signal at the time of the bloom was
weaker. These results imply a consistent onshore flow of water at or before the sampling date,
resulting in retention of the water (and concomitant phytoplankton growth) against the coast.
The discrepancy in the magnitude of the blooms observed at the two stations may relate to an
inequality in the amount of effluent moving north or south against the coast, or dilution of a
single uniform bloom to varying degrees with coastal water.
Secondarily-treated effluent has experimentally been shown to stimulate diatom
communities in previous studies conducted in the region (Seubert et al., 2017), and diatoms were
relatively abundant in samples collected in the weeks prior to the detection of the bloom.
Diatoms can grow at rates >1.0 day
-1
(Eppley, 1972; MacIntyre et al., 2002) and respond quickly
to nutrient inputs, as long as silicate concentrations are sufficient. Silicate concentrations were
elevated at the outfall (Supplementary Figure 3.4) compared to concentrations typical of SMB
157
(Shipe et al., 2008), either from entrainment of bottom water near the outfall, or directly from the
effluent. The development of a diatom bloom, therefore, was likely related to the stimulation of
the existing diatom community following sufficient inoculation of effluent nutrients, including
silicate. Silicate availability may have ultimately controlled the duration and/or magnitude of the
diatom bloom. Silicate concentrations were below the detection limit at the surface at Station
D13W, where the bloom was most intense on 30 September. The exhaustion of available silicate
appeared to cause the demise of the diatom bloom. Chl a concentrations were greatly reduced the
following week on 7 October (Figure 3.3D).
The marine euglenoid bloom (Bloom 2) observed on 14 October and 17 October in the
northern regions of the bay exhibited the highest chlorophyll values observed during the diversion.
Marine euglenoids have been documented in regions of SMB in the past (Stauffer et al., 2013)
and were observed, albeit rarely, in the microplankton prior to the development of the bloom.
Euglenoids are known to respond very rapidly to elevated nutrient conditions (Olli et al., 1996)
and have been documented near effluent outfalls (Stonik and Selina, 2001). Drifter observations
for 16 October showed the general flow direction for plume water towards the locations with
high chl a concentrations on 14 and 17 October (Figure 3.6). The salinities measured on 14
October at the beginning of the bloom were quite low (<33.00 psu) (Figure 3.5) and ammonia
concentrations were >20 µM. These factors both suggest recent effluent impacts in the region.
Elevated ammonia concentrations on 14 October also meant that nitrogen was still readily
available to phytoplankton for growth. Interestingly, the rapid development of the euglenoid
bloom between 14 October and 17 October coincided with one of the periods when effluent was
minimally chlorinated prior to discharge on (14-22 October; Supplementary Figure 3.1). It is
possible that the very low chlorination aided in the rapid response of the euglenoid population to
158
the effluent, although it is unclear why euglenoids in particular thrived. The demise of Bloom 2
coincided with a shift in the direction of movement of the effluent plume. Net downcoast
movement of drifters was observed on 19 October (Figure 3.6D), indicating that the massive
euglenoid population may have exhausted the nutrients remaining at the bloom site.
The bloom observed on 21 October (Bloom 3) was unique because it is the first report of
high abundances of C. marina in the waters of the SCB. Elevated abundances of C. marina are
an environmental and human health concern because species of this genus have been implicated
in fish kills globally and are potential brevetoxin producers (Bourdelais et al., 2002; Imai and
Yamaguchi, 2012). C. marina has been observed within King Harbor in the past (A. Schnetzer,
unpublished data), and was documented in low abundances within the bay prior to Bloom 3. The
C. marina bloom inside King Harbor and at Station D13W appears to be the result of the effluent
plume moving southward from the outfall and into the Harbor (Figure 3.6D). Laboratory studies
have shown that C. marina grows optimally at low salinities (~30 psu) (Marshall and
Hallegraeff, 1999), therefore slightly depressed salinities associated with the effluent may have
favored C. marina growth in a region where it has previously been documented.
Effluent discharge did not result in the development of a widespread or long lasting HAB
event. While a C. marina bloom (Bloom 3) was observed, it was geographically restricted, no
toxins were detected, and the event was relatively short-lived. If the diversion had occurred in
the spring, however, a bloom of Pseudo-nitzschia might have occurred. The most prevalent
regional HAB taxa Pseudo-nitzschia forms toxigenic blooms almost exclusively in the spring
when temperatures are cooler and upwelling is most intense (Smith et al., in press). The timing
of the diversion was purposely planned to occur in the autumn as opposed to the spring to avoid
causing or exacerbating a toxigenic Pseudo-nitzschia bloom. Interestingly, Pseudo-nitzschia was
159
detected in the 30 September bloom but was a relatively minor component of the community
(~1%) and no domoic acid was detected. Pseudo-nitzschia and small concentrations of domoic
acid were detected shortly after the end of the diversion on 5 November when temperatures were
lower (Figure 3.5).
3.4.3 Response of Other Microbial Assemblages to the Diversion
The diversion resulted in a patchy response of the heterotrophic bacteria with large
increases in abundances near the outfall, and locations with elevated chl a concentrations, but a
more muted response away from those regions. Near the outfall and phytoplankton bloom
locations abundances of heterotrophic bacteria increased approximately 1- to 3-fold above pre-
diversion observations. However, in the more offshore regions of the bay, increases in
heterotrophic bacterial abundances were relatively minor. Abundances of heterotrophic bacteria
only showed a 0.3-fold increase from pre-diversion observations when abundances were
averaged across all stations during the diversion, illustrating the patchy nature of the response.
Overall, these observations differ from the large net heterotrophic bacterial (6.7-fold increase in
abundances) response reported in a previous diversion study (Caron et al. 2017).
Cyanobacteria and picoeukaryotic algae showed relatively minimal responses during the
diversion. Cyanobacterial abundances, in general, were higher subsurface and offshore, away
from the regions where effluent impact was strongest. Picoeukaryotic algae exhibited a minimal
response to effluent throughout the diversion, with the exception of the increased abundances
within regions of the euglenoid bloom (Bloom 2) on 17 October. These results differ from the
results presented in Caron et al. (2017), where net increases in cyanobacterial and picoeukaryotic
algal abundances were reported in response to during a nearshore effluent diversion.
160
The microzooplankton response during the diversion was also patchy and largely tracked
the response of the phytoplankton and heterotrophic bacteria to the effluent, particularly during
the last few weeks of the diversion and post-diversion (e.g. surveys beginning on 17 October;
Supplementary Figure 3.2). Abundances of microzooplankton generally remained similar to pre-
diversion observations in locations with minimal bloom activity. Peaks in microzooplankton
abundances were loosely related to increased chl a concentrations and high abundances of
phytoplankton and heterotrophic bacteria (Supplementary Figure 3.5), which suggests that
microzooplankton grazers may have been consuming these groups and could have aided in the
demise of bloom events.
3.4.4 Studies of Nearshore Effluent Discharge in the Central Southern California Bight
Effluent diversions have been conducted in the central SCB region three times within the last
decade, including the present study. Overall, comparisons between this work and past diversion
studies in the region (Reifel et al., 2013; Howard et al., 2017 and references therein) suggest that
the response of plankton communities to effluent may be regionally specific and contingent on
factors such as pre-diversion standing stocks, regional hydrography, wind-driven circulation, and
the nature of the effluent. HTP conducted a short (50 hour) diversion in 2006, during which it
was reported that the discharge resulted in the increased abundance of several HAB
dinoflagellate species within a limited geographic region of the bay (Reifel et al., 2013). The
magnitude of the phytoplankton response to the 2006 diversion was similar to the present study
in that chl a concentrations as high as 100 µg L
-1
were reported. The response of specific
taxonomic groups of phytoplankton during the 2006 diversion, however, was distinct from that
observed in the present study. The dinoflagellates Lingulodinium polyedra, Akashiwo sanguinea,
and Margalefidinium spp. (formerly Cochlodinium spp.) dominated the phytoplankton bloom
161
during the 2006 HTP diversion. Those taxa were dominant in the community prior to the
diversion, and therefore the effluent appeared to cause an increase of species already abundant in
the bay (Reifel et al., 2013). In contrast, each of the three blooms during the present study were
taxonomically distinct and in some cases dominated by taxa that were rare, or not observed prior
to the start of the diversion.
A longer diversion event was conducted during autumn 2012 by the Orange County
Sanitation District (OCSD), located on the San Pedro Shelf (to the south of SMB). The results of
that diversion were quite different from the results of the HTP diversion in 2006 and the present
study. OCSD is a POTW of similar size to HTP that discharges an average of 5.3 x 10
8
L day
-1
of
effluent into the SPS region. The OCSD diversion lasted three weeks during which the response
of the phytoplankton community was a surprisingly minimal (Caron et al. 2017). Survey
observations during the OCSD diversion documented chl a concentrations that were generally <5
µg L
-1
. Additionally, there was little stimulation of HAB forming taxa.
The muted phytoplankton response to the OCSD diversion was attributed to the interplay
of three factors. OCSD conducted enhanced chlorination followed by dechlorination to minimize
the public health risks associated with nearshore effluent discharge. It was reported that
disinfection by-products resulting from this process inhibited photosynthesis and phytoplankton
growth for a period of days (Kudela et al., 2017). Conversely, heterotrophic bacterial abundances
throughout the region increased 6.7-fold from levels measured prior to the diversion. The
nitrogen in the effluent appeared to be immobilized in the bacterial biomass and rendered
temporally unavailable for phytoplankton growth, during which time the plume had sufficiently
dispersed (Caron et al., 2017). Finally, the SPS is a relatively straight coastline (as opposed to a
semi-enclosed bay such as SMB) with relatively energetic alongshore currents. Effluent was
162
quickly advected from the discharge location and diluted, preventing retention of elevated
concentrations of nutrients for extended periods of time (Lucas and Kudela, 2017). Together
these factors appeared to work to minimize the response of the phytoplankton community to
nutrients present in the effluent.
The 2015 HTP diversion in SMB clearly demonstrated that effluent could stimulate large,
taxonomically distinct algal blooms in the nearshore ecosystem, given the temporal and spatial
alignment of inoculum (both of nutrients and biology), time and transport. The hydrography of
SMB is semi-retentive, unlike the SPS, resulting in longer retention of the effluent plume and
‘re-inoculation’ of water masses by tidally-forced circulation. The results of this study, therefore
suggest that plankton dynamics in the SMB, and other regions with semi-retentive circulation
patterns, may be particularly susceptible to the development of large blooms following large
nearshore influxes of anthropogenically-sourced nutrients.
163
3.5 Chapter Three Figures and Tables

Figure 3.1: Map of phytoplankton monitoring stations in Santa Monica Bay showing the
location of the 1.2 km outfall and 8.1 km outfall. A ‘core’ set of stations (indicated by black
circles and labeled with station identifiers) was visited on all weekly surveys during the diversion
of effluent to the 1.2 km outfall. Additional stations occupied to sample features of interest are
also shown. The station shown with a square was visited on 16 September only. Stations shown
with an inverted triangle were visited on 21 October only. Stations shown with diamonds show
where discrete samples were collected on the ad hoc 17 October survey conducted on the M/V
Marine Surveyor. Inset shows California coast with study region identified.
164

Figure 3.2: Chlorophyll a concentrations from discrete water samples collected during all
phytoplankton surveys at (A) the surface and (B) at subsurface depths (subsurface samples were
not collected on 17 October). Black horizontal lines near the top of the panels indicate the period
of time that the effluent was diverted to the 1.2 km outfall. Dotted line shows major bloom
threshold and dashed line shows minor bloom threshold based on criteria reported by Seubert et
al. (2013). Data shown in each panel are from the 16 September survey (n=8), the 23 September
survey (n=10), the 30 September survey (n=10), the 7 October survey (n=10), the 14 October
survey (n=10), the 17 October survey (n=5), the 21 October survey (n=12), the 28 October
survey (n=10), the 5 November survey (n=10), and the 11 November survey (n=10).
a
b
165

Figure 3.3: Chlorophyll a concentrations from samples collected at the surface from all
phytoplankton surveys conducted during the study. Black lines indicate the locations of the 8.1
km and 1.2 km outfalls. Dates shown in the panels are (A) 16 September survey (pre-diversion),
a b c
d e f
j
g h i
Chlorophyll a (µg l
-1
)
≥20
166
(B) 23 September survey (3 days into diversion), (C) 30 September survey (9 days into
diversion), (D) 7 October survey (16 days into diversion), (E) 14 October survey (23 days into
diversion), (F) 17 October survey (26 days into diversion), (G) 21 October survey (30 days into
diversion), (H) 28 October survey (37 days into diversion), (I) 5 November survey (3 days post-
diversion), and (J) 11 November survey (6 days post-diversion).

167

Figure 3.4: Box and whisker plots showing cell abundances (cells l
-1
) at all stations and depths
sampled on each date: (A) total microplankton cell types counted via microscopy, (B)
heterotrophic bacteria, (C) photosynthetic cyanobacteria (Prochlorococcus and Synnecococus
combined), and (D) picoeukaryotic algae, the latter three groups enumerated using flow
cytometry. Black horizontal lines near the top of each panel indicate the period of time that the
a
b
c d
Heterotrophic Bacteria Total Microplankton
Cyanobacteria Picoeukaryotes
168
effluent was diverted to the 1.2 km outfall. Box and whisker plots were constructed in
SigmaPlot. The ‘box’ shows the distribution of the data from the calculated interquartile range
(IQR) from each date (shown as a rectangle, top of the rectangle is the 75th percentile and the
bottom of the rectangle is the 25th percentile). The median of each sampling date is shown as a
horizontal line within each box. The bottom and top ‘whiskers’ are the minimum and maximum
of the 10th and 90th percentile ranges (shown as vertical lines extending from the ‘box’).
Outliers are shown as black circles and were identified in SigmaPlot as data points that fell
outside of the 10th and 90th percentile ranges.

169

Figure 3.5: Chlorophyll a concentrations from all surface samples plotted on a temperature-
salinity diagram in order to visualize the impact of the effluent plume on bloom development.
Chl a concentrations of <10 µg l
-1
(sub-bloom concentrations) are plotted in blue, while chl a
concentrations of >10 µg l
-1
(indicative of a minor bloom or greater) are plotted in red. Samples
collected at Station D9W, the location of the 1.2 km outfall diffuser, during the diversion are
enclosed in a dashed oval. Samples with major bloom concentrations of chlorophyll are enclosed
by solid ovals and annotations indicating Blooms 1, 2 and 3. Samples with minor bloom chl a
concentrations are indicated with dashed lines and annotations indicating the sample date and
location.
D9W
Salinity (psu)
Potential Temperature (°C)
Bloom 1
5 Nov D13W
Oct 28 D2W
Bloom 2
Bloom 3
170

Figure 3.6: Microstar drifter tracks from (A) 28-29 September, (B) 14 October, (C) 16 October
and (D) 19-21 October. The geographic regions shown in panels were scaled to best show drifter
trajectories and panels (A) and (D) show slightly different geographic regions than panels (B)
and (C). Open circles and notations indicate the nearest phytoplankton monitoring station for
orientation. Panels show trajectories of multiple drifter deployments, as indicated by multiple
D5W
118.48 118.46 118.44 118.42
33.96
33.94
33.92
33.90
Oct 14
08:10
10:10
12:10
14:10
local time (hr; Oct 14)
D13W
Oct 19 - 21
118.48 118.44 118.40
33.88
33.92
33.96
00:00
06:00
12:00
18:00
00:00
06:00
12:00
18:00
00:00
06:00
12:00
local time (hr; Oct 19 - 21)
Sept 28 - 29
D13W
118.48 118.44 118.40
33.88
33.92
33.96
14:00
20:00
02:00
08:00
14:00
local time (hr; Sept 28 - 29)
D5W
Oct 16
33.96
33.94
33.92
33.90
118.42 118.44 118.46 118.48
08:00
10:00
12:00
local time (hr; Oct 16)
a
c
d
b
171
lines. Deployment times of each drifter varied on an individual basis, with retrieval of drifters
usually occurring when beaching appeared imminent. The red dot in each panel indicates the
location of the 1.2 km outfall, and the starting location of a majority of drifter deployments
shown. Green dots in panels (A) and (D) indicate the beginning of the drifter trajectory if a
drifters was deployed away from the outfall. The color bar on each panel indicates the sampling
time of each trajectory, with the start of the trajectory indicated with the color blue. The scaling
of the color bar on each panel is different depending on the length of the deployment presented.
Panel (A) and (D) show multi-day trajectories lasting ~30 hours and ~ 60 hours, respectively.
Panels (B) and (C) show single day trajectories of ~6 hours.
172

Figure 3.7: Hierarchical clustering of the Bray-Curtis similarity values of plankton community
composition of discrete samples that exceeded 10 µg l
-1
of chlorophyll a (indicative of a minor
bloom or larger). Microplankton abundances from inverted microscopy counts were utilized for
this analysis. Samples collected from Bloom 1 (diatom bloom) are enclosed in a box with a solid
line, samples collected during Bloom 2 (euglenoid bloom) are enclosed in a box with a dashed
line and samples collected during Bloom 3 (Chattonella marina bloom) are enclosed in a box
with a dotted line. Thick black lines indicate significant differences between communities
(SIMPROF, p < 0.05), and dashed black lines indicate non-significant differences between
communities (SIMPROF, p > 0.05).

173
Table 3.1: Temperature, salinity and dissolved nutrient ranges observed in Santa Monica Bay
prior to (Pre-Div), during (Diversion) and following (Post-Div) the effluent diversion to the 1.2
km outfall pipe. All dissolved nutrients are reported in µM. Salinity is reported in psu. The table
includes all data collected during shipboard surveys except the ad hoc survey on 17 October.
Temp = temperature in °C, n = number of measurements, BD = below the detection limit of the
method, DNQ = detected, but not quantified, NA = not analyzed.

Surface
Temp n Salinity n NH
3
n NO
3
-
n PO
4
3-
n SiO
4
2-
n
Pre-Div 23.2-24.6 8 32.50-33.24 8 BD-2.86 8 DNQ 8 BD 8 2.48 1
Diversion 20.8-25.3 62 31.10-33.48 62 BD -206 62 BD -2.86 62 BD -5.17 62 BD -32.8 17
Post-Div 17.5-19.0 20 33.30-33.45 20 BD 20 BD -2.14 20 BD 20 NA 0
Subsurface
Temp n Salinity n NH
3
n NO
3
-
n PO
4
3-
n SiO
4
2-
n
Pre-Div 17.6-22.4 8 33.28-33.45 8 BD 8 DNQ 8 BD 8 6.57 1
Diversion 15.2-24.8 62 33.05-33.48 62 BD-14.3 62 BD -2.14 62 BD 62 BD-17.3 14
Post-Div 16.3-18.6 20 33.30-33.40 20 BD-10.0 20 BD -2.14 20 BD 20 NA 0
174
Table 3.2: Results of Spearman rank order analyses between chlorophyll a (chl a) and physical
and chemical parameters from all shipboard surveys except the ad hoc survey conducted on 17
October. These analyses were conducted with data collected during the diversion only, and
repeated using all data collected during the study. Bolded values are significant at p < 0.05; n =
number of data points in the analysis.

Temperature Salinity NH
3
NO
3
-
PO
4
3-
n
Chl a (all samples) -0.130 -0.364 0.263 -0.047 0.149 178
Chl a (diversion only) -0.061 -0.511 0.319 -0.036 0.174 124
175
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Trinh, R.C., Fichot, C.G., Gierach, M.M., Holt, B., Malakar, N.K., Hulley, G., Smith, J., 2017.
Application of Landsat 8 for Monitoring Impacts of Wastewater Discharge on Coastal
Water Quality. Front Mar Sci 4(329).
Uchiyama, Y., Idica, E.Y., McWilliams, J.C., Stolzenbach, K.D., 2014. Wastewater effluent
dispersal in Southern California bays. Cont Shelf Res 76, 36-52.
Utermöhl, H., 1958. Zur vervollkommnung der quantitativen phytoplankton-methodik. Mitt. int.
Ver. theor. angew. Limnol. 9, 1-38.
Washburn, L., Jones, B.H., Bratkovich, A., Dickey, T., Chen, M.-S., 1992. Mixing, dispersion,
and resuspension in vicinity of ocean wastewater plume. J Hydraul Eng 118(1), 38-58.
180
Supplementary Materials
Supplementary Material for Chapter One
Supplementary Materials and Methods

Study Location and Regional Definitions
The coastline of California is directly bordered by fifteen counties, and traditionally is further
subcategorized into the northern coast, central coast and southern coast regions of California.
The north Coast of California includes Del Norte County, Humboldt County, Mendocino
County, Sonoma County, Marin County, San Francisco County, and San Mateo County. Santa
Cruz County, Monterey County, and San Luis Obispo County comprise the central Coast. The
counties bordering the Southern California Bight region are Santa Barbara County, Ventura
County, Los Angeles County, Orange County and San Diego County.
For several different analyses, data from the Bight were divided into the northern and
southern regions. The northern region of the Bight was geographically defined here as the
coastline bordering Santa Barbara County and Ventura County and the southern region was
defined as the coastline bordering Los Angeles County, Orange County and San Diego County.

Data Sources for Pseudo-nitzschia Cell Abundance and Domoic Acid in the Particulate Fraction
Particulate domoic acid and Pseudo-nitzschia cell abundance information was synthesized
from the sources described in Supplementary Table 1.2 and represented data collected during
the period of October 2003 to June 2017. Altogether, 4,579 particulate measurements were
summarized. Collection of particulate domoic acid samples varied by study and sample analyses
were conducted via LC-MS or ELISA, depending on the study from which measurements were
obtained.
181
Physiochemical and biological data collected in conjunction with particulate domoic acid
measurements and Pseudo-nitzschia cell abundances included temperature, salinity, chlorophyll
a concentrations, dissolved NO
3
3-
concentrations, dissolved PO
4
3-
concentrations, and dissolved
SiO
4
2-
concentrations. Measurement of these parameters varied by study and detailed
methodologies can be found in the references listed in Supplementary Table 1.2.
Domoic acid concentrations in shellfish were obtained from the California Department of
Public Health Center of Environmental Health (2014). A total of 4,528 measurements were
reported from the period of January 2003 to December 2016. Shellfish data was sorted into
general region in California, either the northern, central or southern coast based on county where
the tested organism was collected. The Southern coast was further subdivided into the northern
counties of southern California and the southern counties of southern California.

Marine Animal Stranding and Poisoning Data
Most samples for domoic acid analysis in marine mammals were provided by the Marine
Mammal Care Center in San Pedro, Los Angeles County, and the Pacific Marine Mammal Care
Center in Laguna Beach, Orange County. Samples were collected upon arrival at the Centers and
occasionally subsequent to care or at the time of necropsies for domoic acid analysis.
Methodological analyses for these specimens were investigated by Seubert et al. (2014) to
examine and characterize matrix effects. Most samples for domoic acid analysis in seabirds
were obtained from the International Bird Rescue center in San Pedro, and the California
Wildlife Center, Malibu, both in Los Angeles County. Matrix effects have not been well-
determined for some bird fluids and solids, and therefore the absolute values of domoic acid
concentrations have not been vetted through carefully controlled investigations. All unpublished
182
marine mammal and seabird data are available from the authors.

Temperature Records in the Southern California Bight
Historical sea surface temperature data was obtained from NOAA National Data Buoy
Center (http://www.ndbc.noaa.gov/) for Station 46222 located in San Pedro, CA (33.618 N
118.317 W) for the period of March 16, 2007 to May 1, 2007. Measurements were averaged by
day and reported as a mean daily temperature.
Temperature data for Newport Beach Pier and Stearns Wharf were obtained from
http://sccoos.org/data/autoss/ for the date of first measurement (June 17, 2005 at Newport Beach
Pier and September 16, 2005 at Stearns Wharf) to June 1, 2017. Sensed temperature data was
first quality controlled by removing any data with a primary or secondary flag. Following quality
control protocols, sensed data was averaged by individual day to create a daily mean temperature
and by month across the analysis period. Temperature anomalies were calculated by subtracting
daily mean temperature from the monthly average according to which day the daily temperature
was made. Temperature anomaly data from Newport Beach Pier was compared to particulate
domoic acid measurements above 1 µg L
-1
from the southern region of the Bight. Similarly,
temperature anomaly data from Stearns Wharf was compared to particulate domoic acid
measurements above 1 µg L
-1
from the northern region of the Bight. At both locations,
particulate domoic acid concentrations were temporally matched to positive or negative
temperature anomalies, and are plotted with the respective anomaly in Figure 1.9.

Riverine Discharge into the southern region of the Bight
The role of riverine discharge into the southern region of the Southern California Bight was
183
examined as potential driver of variation in annual domoic acid concentrations.
Average monthly discharge data was obtained from the USGS database
(https://waterdata.usgs.gov/ca/nwis/) for the San Gabriel River (Site: 11085000), Santa Ana
River (Site: 11078000) and Los Angeles River (Site: 11092450), which are the main contributors
of river discharge for the region. The monthly averages of the three rivers were summed and
combined by year to represent the yearly discharge. The median discharge for the year was
calculated for the period of 2003 to 2015. Maximal and mean particulate domoic acid
concentrations were calculated from detectable domoic acid concentrations collected within the
southern region of the Bight and compared to the total yearly riverine discharge.

Climatic forcing of domoic acid events in the Southern California Bight
Long-term trends in domoic acid concentrations were examined in relation to climatic
indices. The multivariate ENSO index (MEI), north pacific gyre oscillation (NPGO), and pacific
decadal oscillation (PDO) were used in these analyses. The monthly MEI was obtained from
http://www.esrl.noaa.gov/psd/enso/mei/table.html, the monthly NPGO was obtained from
http://www.o3d.org/npgo/npgo.php, and the monthly PDO was obtained from
http://research.jisao.washington.edu/pdo/. Particulate domoic acid measurements above the limit
of detection were matched by month to index values and then separated into two groups based on
if the respective climatic index was in the positive or negative phase. The two groups of pDA
concentrations were compared using the Mann-Whitney Rank Sum Test and were determined to
be significantly different at p < 0.05. Statistical analyses were performed in SigmaPlot (v.13.0,
Systat Software, San Jose, CA).
184

Supplemental Figure 1.1. Summary of particulate domoic acid concentrations in plankton
samples presented in Figure 1.3, showing the distribution of values for samples testing above the
limit of detection (approximately 0.01 µg DA L
-1
).

Supplemental Figure 1.2. Monthly distribution of samples collected from 2003 to 2017 by
month that contributed to the particulate domoic acid concentrations presented in Figures 3 and
4.
185

Supplemental Figure 1.3. Relationship between particulate domoic acid (A) or abundances of
Pseudo-nitzschia (B) and water temperature. Panel (A) represents 3,356 data points and panel
(B) represents 2,976 data points collected from throughout the Southern California Bight.

186

Supplemental Figure 1.4. Relationships between particulate domoic acid and (A) Pseudo-
nitzschia cell abundance, (B) phosphate, (C) chlorophyll a, (D) nitrate and (E) silicate.
Approximately 2,330 data points from throughout the Southern California Bight were
summarized.

187

Supplemental Figure 1.5. Advanced Very High Resolution Radiometer (AVHRR) image of sea
surface temperature (A) and MODIS-Aqua image of chlorophyll fluorescence in the Southern
California Bight on April 8, 2010. Note lower water temperatures and higher chlorophyll
concentrations in the Santa Barbara Channel in the northern region of the Southern California
Bight compared to warmer temperatures and lower chlorophyll fluorescence values in the central
and southern regions of the Bight.

188

Supplemental Table 1.1. Maximal domoic acid concentrations each year since 2003, and their
month of occurrence, in the northern counties (Santa Barbara, Ventura) and southern counties
(Los Angeles, Orange and San Diego) within the Southern California Bight. Months of maximal
concentrations for each year are shown as colored arrows in Figure 1.4.

Year Month pDA (µg L
-1
) Region
2003 May 12.73 Southern Counties
2003 May 1.68 Northern Counties
2004 February 2.33 Southern Counties
2004 January 1.06 Northern Counties
2005 April 2.91 Southern Counties
2005 June 49.13 Northern Counties
2006 March 14.39 Southern Counties
2006 April 1.58 Northern Counties
2007 April 26.97 Southern Counties
2007 April 0.36 Northern Counties
2008 April 19.74 Southern Counties
2008 August 0.10 Northern Counties
2009 May 8.11 Southern Counties
2009 November 5.45 Northern Counties
2010 May 4.62 Southern Counties
2010 September 6.13 Northern Counties
2011 March 52.30 Southern Counties
2011 April 25.92 Northern Counties
2012 April 0.63 Southern Counties
2012 July 19.01 Northern Counties
2013 April 17.40 Southern Counties
2013 April 4.42 Northern Counties
2014 May 1.60 Southern Counties
2014 May 0.63 Northern Counties
2015 May 0.10 Southern Counties
2015 June 0.45 Northern Counties
2016 July 0.05 Southern Counties
2016 April 0.07 Northern Counties
2017 May 14.4 Southern Counties
2017 April 7.56 Northern Counties
189
Supplemental Table 1.2. Summary of data sources for Pseudo-nitzschia cell abundances and
particulate domoic acid concentrations summarized in this study. All unpublished data are
available from the authors.

Date Range Location Source/Citation
Oct 2003 - Jun 2010
Northern SCB
(Santa Barbara Channel)
Anderson et al. (2009)
Anderson et al. (2011)
Dec 2003 - Feb 2004
Southern SCB
(San Diego)
Busse et al. (2006)
May 2003 – March 2004
Central SCB
(San Pedro Shelf & LA)
Schnetzer et al. (2007)
Mar 2005 – Dec 2007
Central SCB
(San Pedro Shelf & LA)
Schnetzer et al. (2013)
Feb - May 2006; Feb - May
2007; Feb - May 2009
Central SCB
(San Pedro Shelf & LA)
Unpublished.
Mar – Apr 2008; Feb - Jul
2009; Mar 2011
Central SCB
(San Pedro Channel)
Stauffer et al. (2011);
Partially unpublished.
Jun 2008 – Jun 2017
Bight-wide in SCB
(San Diego, Newport
Beach, Santa Monica,
Santa Barbara)
Publicly available at
http://www.sccoos.org/data/ha
bs/
Mar 2010 to Apr 2010 Bight-wide in SCB
Published white paper.
http://www.sccwrp.org/Resear
chAreas/
2010-2013
Northern SCB
(Santa Barbara Channel)
Unpublished (C.R. Anderson)
Feb 2010 – Nov 2012
Central SCB
(Redondo Beach Pier)
Seubert et al. (2013)
Partially unpublished.
Sep 2012 – Nov 2012
Central SCB
(San Pedro Channel)
Caron et al. (2017)
Nov 2012 – Feb 2014
Central SCB
(Cabrillo Marina Pier)
Unpublished.
Mar - Apr 2013;
Apr - May 2014
Central SCB
(San Pedro Channel)
Smith et al. (2018)
Apr - Jun 2017
Northern & Central SCB
(San Pedro Channel &
Santa Barbara Channel)
Unpublished.
NOAA Event Response.
190
Supplementary Material for Chapter Two
Supplementary Figure 2.1: Data obtained from shipboard CTD downcasts from 5 April 2013
and 2 April 2014 from all stations. A - D) Temperature (°C), salinity (PSU), dissolved oxygen
(mL L
-1
), and chlorophyll a (µg L
-1
) respectively from 5 April 2013 survey. E - H) Temperature
(°C), salinity (PSU), dissolved oxygen (mL L
-1
), and chlorophyll a (µg L
-1
) respectively from 2
April 2014 survey. The solid line indicates data from Station 1, the dots indicate data from
Station 2, the squares indicate data from Station 3, and the dashes indicate data from Station 4.

Supplementary Figure 2.2: Discrete dissolved nutrient concentrations of NO
x
-
, NH
4
+
, Urea,
PO
4
3-
, and SiO
4
2-
by survey date. All concentrations are in µM. Black lines represent the average
concentration for all stations and depths from each survey date.
0
50
100
150
9.5 11.5 13.5 15.5
0
50
100
150
33.4 33.6 33.8 34 34.2
0
50
100
150
2 4 6
0
50
100
150
0 2 4 6 8 10 12
0
50
100
150
9.5 11.5 13.5 15.5
0
50
100
150
33.4 33.6 33.8 34 34.2
0
50
100
150
2 4 6
0
50
100
150
0 2 4 6 8 10 12
a b c d
e
f
g h
Temperature (°C) Salinity (PSU) Oxygen (mL L
-1
) Chlorophyll a (µg L
-1
)
191

Supplementary Figure 2.3: MDS plot using Euclidean distance calculations for the
environmental variables (temperature, NO
x
-
, NH
4
+
, Urea, PO
4
3-
, and SiO
4
2-
, N:P, N:Si and Si:P).
The distance between points indicates dissimilarity between points, with larger distance
indicating a greater difference between samples. Individual points represent one sample
collected on a given cruise date. Individual points were factored by cruise date, regardless of
station or depth. Salinity was removed as a variable from the analysis due to the number of
missing samples. Two samples from the 7 April 14 cruise were removed from the analysis due to
missing NO
x
-
, PO
4
3-
, and SiO
4
2-
measurements. The analysis was performed using PRIMER v6
(Clarke and Gorley, 2006). The data matrix was normalized with a square root transformation
prior to Euclidean distance calculations.
NO
3
(µM)
2013
NH
4
(µM) Urea (µM) PO
4
(µM) Si (µM)
2014
192

Supplementary Table 2.1: ARISA fragments detected in all samples analyzed in this study with
corresponding species identifications, reference sequences used for identification, the origin of
the reference sequence, and the observed toxin-production by the species.
ARISA
Peak
Size
ARISA ID
Reference
sequence
used for
putative
ARISA ID
Origin of
reference
sequence
Species
Toxigenic
Reference for Toxin
Produciton
138 P. sabit KP288511
Eastern
Pacific
No Teng et al., 2015
140 P. galaxiae
MG19595
0*
San Pedro
Bight
Yes/No
Lundholm and Moestrup
2002;
Cerino et al., 2005
142 P. pungens
DQ996020
Puget Sound,
WA
Yes Rhodes et al. 1996
144 P. multiseries
AY257844
Monterey
Bay, CA
Yes Bates et al.,1989
150
P. australis/P.
seriata
DQ996023 /
DQ996022
Monterey
Bay, CA/
Puget Sound,
WA
Yes
Fritz et al.,1992; Garrison
et al., 1992;
Lundholm et al., 1994
195/19
6
P. heimii/P.
americana
EU051655 /
KR053127
NE Subarctic
Pacific/
Pacific NW
No Scholin et al., 1999
207 P. fryxelliana
JN050288 Pacific NW
No/Not
Tested
Lundholm et al., 2012
209 P. decipiens
JF308599 Pacific NW
No Lundholm et al., 2006
223/22
4
Unk. 223/224
n/a n/a
Unknown -
233 P. cuspidata
JN050289 Pacific NW
Yes Trainer et al., 2009
*Denotes sequence obtained in the present study.
193
Supplementary Material for Chapter Three

Supplementary Figure 3.1: Total chlorine residual (mg l
-1
) measured in the effluent at the 1.2
km Wet-Well (effluent storage location in the plant prior to discharge from the outfall) for the
period of the diversion spanning 21 September to 1 November. Total chlorine residual was not
measured on the final day of the diversion, 2 November.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
21-Sep
23-Sep
25-Sep
27-Sep
29-Sep
1-Oct
3-Oct
5-Oct
7-Oct
9-Oct
11-Oct
13-Oct
15-Oct
17-Oct
19-Oct
21-Oct
23-Oct
25-Oct
27-Oct
29-Oct
31-Oct
Total Chlorine Residual (mg l
-1
)

194

Supplementary Figure 3.2: Box and whisker plot showing microzooplankton abundances (cells
l
-1
) at all stations and depths sampled on each date as determined via microscopy. The black bar
near the top of the plot indicates the period of time that the effluent was diverted to the 1.2 km
outfall. The box and whisker plot was assembled using the same procedure described in the
legend of Figure 3.4.
195

Supplementary Figure 3.3: Chlorophyll a concentrations versus corresponding salinity
measurements at Station D13W (n = 6). The equation of the regression line is y = -139.11x +
4645.53 R² = 0.98, p < 0.0001.

196

Supplementary Figure 3.4: Surface dissolved SiO
4
2-
concentrations (µM) measured during the
pre-diversion and diversion periods of the study (n = 18). Station identifiers are described in the
legend. The black bar near the top of the plot indicates the period of time that the effluent was
diverted to the 1.2 km outfall. Pre-diversion SiO
4
2-
concentrations were 2.48 µM at the outfall
location (Station D9W), and increased during the diversion to 21.7±6.8 µM, (n = 5) at the same
location.
0
5
10
15
20
25
30
35
40
9/16/15 9/23/15 9/30/15 10/7/15 10/14/15 10/21/15
SiO
4
2-
(µM)
D2W D4W D5W D9W (Outfall) D13W King Harbor DD17B
197

Supplementary Figure 3.5: Linear regressions of a) microzooplankton abundances versus
corresponding heterotrophic bacterial abundances (n = 183), b) microzooplankton abundances
versus corresponding phytoplankton abundances (n = 183), and c) microzooplankton abundances
versus corresponding chlorophyll a concentrations (n = 183). Phytoplankton abundances were
determined by the summation of the abundances of known phytoplankton genera within each
discrete sample. The equation of the regression line in a) is y = -1214.43x + 8.63 x 10
-6
R² =
0.22, p < 0.0001. The equation of the regression line in b) is y = 9314.40x + 1.07 x 10
-3
R² =
0.54, p < 0.0001. The equation of the regression line in c) is y = 8723.40x + 408.79 R² = 0.23, p
< 0.0001.

Abstract (if available)

Abstract Microalgae are primary producers that serve a vital ecological role in marine food webs through the transfer of energy to higher trophic levels. While microalgae generally benefit higher trophic levels, some blooms of microalgae can disrupt their local ecosystems through the production of toxins or other negative ecosystem effects

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Asset Metadata

Creator Smith, Jayme (author)

Core Title Examining potential triggers of algal blooms and harmful algae in the Southern California bight region

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology (Marine Biology and Biological Oceanography)

Publication Date 07/25/2018

Defense Date 05/15/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag coastal water quality,domoic acid,effluent diversion,harmful algal bloom,OAI-PMH Harvest,phytoplankton,Pseudo-nitzschia,Southern California Bight

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Caron, David A (committee chair), Capone, Douglas (committee member), Hutchins, David (committee member), Sukhatme, Gaurav (committee member)

Creator Email jaymesmi@usc.edu,jbadoud@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-30346

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